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"docs": {"2008.cikm_conference-2008.76": {"snippet": "...tion retrieval\n\n\n ABSTRACTAs an effective technique for improving retrieval effectiveness, relevance feedback (RF) has been widely studied in both monolingual and cross-language information retrieval ...", "weights": [[8, 17, 1.0], [59, 68, 1.0], [69, 78, 1.0], [79, 92, 1.0], [193, 202, 1.0]]}, "2013.ipm_journal-ir0volumeA49A1.18": {"snippet": "...mage retrieval from multimedia databases, which improves both the effectiveness and efficiency of traditional CBIR by exploring secondary media. We perform retrieval in a two-stage fashion: first rank...", "weights": [[8, 17, 1.0], [69, 82, 1.0], [159, 168, 1.0]]}, "2008.cikm_conference-2008.183": {"snippet": "... the effectiveness of the opinion retrieval and opinion polarity classification\n\n\n ABSTRACTOpinion retrieval is a document retrieving and ranking process. A relevant document must be relevant to the q...", "weights": [[8, 21, 1.0], [37, 46, 1.0], [102, 111, 1.0]]}, "2019.tois_journal-ir0volumeA37A1.2": {"snippet": "...ms \u2192 Retrieval effectiveness; Learning to rank; Information retrieval query processing;Additional Key Words and Phrases: Information systems, information retrieval, learning to rank, retrieval system ...", "weights": [[8, 17, 1.0], [18, 31, 1.0], [63, 72, 1.0], [157, 166, 1.0], [186, 195, 1.0], [196, 202, 1.0]]}, "P92-1014": {"snippet": "...tion retrieval system which uses advanced natural language processing techniques to enhance the effectiveness of traditional key-word based document retrieval. The backbone of our system is a statisti...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [99, 112, 1.0], [152, 161, 1.0], [183, 189, 1.0]]}, "2002.ipm_journal-ir0volumeA38A1.0": {"snippet": "Improving the retrieval effectiveness of very short queries", "weights": [[0, 9, 1.0], [14, 23, 1.0], [24, 37, 1.0]]}, "2017.ipm_journal-ir0volumeA53A4.6": {"snippet": "...arch effectiveness for different kinds of users? We observed four different kinds of information seekers using an experimental information retrieval system: (1) search novices; (2) domain experts; (3)...", "weights": [[8, 21, 1.0], [142, 151, 1.0], [152, 158, 1.0]]}, "2005.trec_conference-2005.26": {"snippet": "... our retrieval system scales up to terabyte-scale collections. We found that our retrieval system can handle 25 million documents, although in terms of indexing time we are approaching the limits of a...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [84, 93, 1.0], [94, 100, 1.0]]}, "2017.sigirconf_conference-2017.144": {"snippet": "... the effectiveness of information retrieval systems by augmenting documents' term probability estimates with those of similar documents, producing higher quality document representations. We propose a...", "weights": [[8, 21, 1.0], [37, 46, 1.0]]}, "2014.cikm_conference-2014.212": {"snippet": "...mean retrieval effectiveness (e.g., MAP) may sacrifice the retrieval stability across queries, implying a tradeoff between effectiveness and stability. The evaluation of both effectiveness and stabili...", "weights": [[8, 17, 1.0], [18, 31, 1.0], [62, 71, 1.0], [126, 139, 1.0], [178, 191, 1.0]]}, "2012.sigirconf_conference-2012.134": {"snippet": "...tion retrieval\n\n\n ABSTRACTMany information retrieval (IR) techniques have been proposed to improve the performance, and some combinations of these techniques has been demonstrated to be effective. How...", "weights": [[8, 17, 1.0], [46, 55, 1.0]]}, "2007.ipm_journal-ir0volumeA43A1.13": {"snippet": "...tion retrieval systems to effectively search for and locate the required information. In this study, we present several improvements over the two main bottlenecks in a distributed information retrieva...", "weights": [[8, 17, 1.0], [195, 204, 1.0]]}, "2008.ipm_journal-ir0volumeA44A3.9": {"snippet": "...tion retrieval systems\n\n\n AbstractNowadays, access to information requires managing multimedia databases effectively, and so, multi-modal retrieval techniques (particularly images retrieval) have beco...", "weights": [[8, 17, 1.0], [141, 150, 1.0], [183, 192, 1.0]]}, "1998.sigirconf_conference-98.33": {"snippet": "...tion retrieval model, in a workable system. First order logic and standard information retrieval techniques are combined together, to the same effect: obtaining an expressive system, able to accuratel...", "weights": [[8, 17, 1.0], [39, 45, 1.0], [90, 99, 1.0], [178, 184, 1.0]]}, "1990.jasis_journal-ir0volumeA41A5.0": {"snippet": "... the effectiveness of information retrieval systems using simulated queries", "weights": [[8, 21, 1.0], [37, 46, 1.0]]}, "1998.sigirconf_conference-98.39": {"snippet": "...heir retrieval strategies. To be viable as a laboratory tool, a collection must reliably rank different retrieval variants according to their true effectiveness. In particular, the relative effectiven...", "weights": [[8, 17, 1.0], [107, 116, 1.0], [150, 163, 1.0], [193, 206, 1.0]]}, "2012.sigirjournals_journal-ir0volumeA46A1.9": {"snippet": "... the effectiveness of retrieval models from the documents retrievability point of view. We focus particularly on the retrieval bias of different retrieval models, and try to examine to what extent thi...", "weights": [[8, 21, 1.0], [25, 34, 1.0], [120, 129, 1.0], [148, 157, 1.0]]}, "2008.cikm_conference-2008.59": {"snippet": "...tion Retrieval (IR) has long focused on effectiveness and efficiency. However, new and emerging access tasks now demand alternative evaluation measures which go beyond this traditional view. A retriev...", "weights": [[8, 17, 1.0], [43, 56, 1.0], [196, 205, 1.0]]}, "2004.clef_workshop-2004w.28": {"snippet": "...e to Retrieval Effectiveness?\n\n\n Named entities in topics are a major factor contributing to the quality of retrieval results. In this paper, we report on an analysis on the correlation between the nu...", "weights": [[8, 17, 1.0], [18, 31, 1.0], [111, 120, 1.0]]}, "2005.ipm_journal-ir0volumeA41A4.12": {"snippet": "...tion retrieval systems offering automated assistance could greatly improve search effectiveness by suggesting or implementing assistance automatically. A critical issue in designing such systems is de...", "weights": [[8, 17, 1.0], [85, 98, 1.0]]}, "1999.cikm_conference-99.43": {"snippet": "...ious retrieval approaches and query representations can improve search effectiveness. Today, many me&search engines exist which combine the results of various search engines in the hopes of improving ...", "weights": [[8, 17, 1.0], [74, 87, 1.0], [193, 202, 1.0]]}, "2012.wwwconf_conference-2012c.84": {"snippet": "...ning system for unrecognized drug side effects discovery based on the text information gathered from the Internet. The system can automatically build a knowledge base for drug side effects by integrat...", "weights": [[8, 14, 1.0], [122, 128, 1.0]]}, "1989.ipm_journal-ir0volumeA25A4.2": {"snippet": "Improving the effectiveness of retrieval systems by information structures", "weights": [[0, 9, 1.0], [14, 27, 1.0], [31, 40, 1.0]]}, "2009.cikm_conference-2009.277": {"snippet": "... for improving retrieval effectiveness, but the noise in the top retrieved documents still can cause topic drift problem that affects the performance of certain topics. By viewing a document as an int...", "weights": [[8, 17, 1.0], [18, 27, 1.0], [28, 41, 1.0]]}, "W05-0704": {"snippet": "...n IR effectiveness and that context sensitive morphology further improves retrieval effectiveness, but the improvement is not statistically significant. Furthermore, the improvement obtained by the us...", "weights": [[8, 21, 1.0], [77, 86, 1.0], [87, 100, 1.0]]}, "2013.ictir_conference-2013.3": {"snippet": "... the retrieval model used in the search engine. Develoing optimal retrieval models has always been a very important fundamental research problem in information retrieval because an improved general re...", "weights": [[8, 17, 1.0], [69, 78, 1.0], [163, 172, 1.0], [201, 210, 1.0]]}, "2001.ipm_journal-ir0volumeA37A4.0": {"snippet": "Retrieval effectiveness on the web", "weights": [[0, 9, 1.0], [10, 23, 1.0]]}, "2014.cikm_conference-2014.9": {"snippet": "...ween Retrieval Bias and Retrieval Performance\n\n\n ABSTRACTRetrievability provides an alternative way to assess an Information Retrieval (IR) system by measuring how easily documents can be retrieved. R...", "weights": [[8, 17, 1.0], [27, 36, 1.0], [128, 137, 1.0], [143, 149, 1.0]]}, "2011.tois_journal-ir0volumeA29A2.0": {"snippet": "...good retrieval performance is closely related to the use of various retrieval heuristics. We connect the weaknesses and strengths of a retrieval function with its implementations of these retrieval he...", "weights": [[8, 17, 1.0], [71, 80, 1.0], [138, 147, 1.0], [191, 200, 1.0]]}, "2010.irfc_conference-2010.7": {"snippet": "...y in Effectiveness of a Retrieval System", "weights": [[8, 21, 1.0], [27, 36, 1.0], [37, 43, 1.0]]}, "2004.cikm_conference-2004.48": {"snippet": "...hile improving retrieval effectiveness. The algorithm is efficient and scalable, suited for retrieval systems managing large document collections.", "weights": [[8, 17, 1.0], [18, 27, 1.0], [28, 41, 1.0], [95, 104, 1.0]]}, "2017.airs_conference-2017.1": {"snippet": "Improving Retrieval Effectiveness for Temporal-Constrained Top-K Query Processing", "weights": [[0, 9, 1.0], [10, 19, 1.0], [20, 33, 1.0]]}, "O07-2010": {"snippet": "...\u4e4b\u7814\u7a76 (Improving Retrieval Effectiveness by Document Reranking and Local Expansion) [In {C}hinese]", "weights": [[8, 17, 1.0], [18, 27, 1.0], [28, 41, 1.0]]}, "2018.ictir_conference-2018.20": {"snippet": "...d by retrieval systems in terms of Information Theory. The proposed framework allows to formalize: (i) system effectiveness as an information theoretic similarity between system outputs and human asse...", "weights": [[8, 17, 1.0], [106, 112, 1.0], [113, 126, 1.0], [174, 180, 1.0]]}, "2005.sigirjournals_journal-ir0volumeA39A2.12": {"snippet": "...s of improving the effectiveness of current information retrieval systems. This aim can be achieved by accomplishing numerous supporting objectives.", "weights": [[8, 17, 1.0], [22, 35, 1.0], [59, 68, 1.0]]}, "O01-2005": {"snippet": "Improving the Effectiveness of Information Retrieval with Clustering and Fusion\n\n\n Fusion and clustering are two approaches to improving the effectiveness of information retrieval. In fusion, ranked l...", "weights": [[0, 9, 1.0], [14, 27, 1.0], [43, 52, 1.0], [127, 136, 1.0], [141, 154, 1.0], [170, 179, 1.0]]}, "1998.sigirconf_conference-98.22": {"snippet": "... our system that included a 3-D visualization.The questions we investigated in the context of this work were:Can we build a system for the aspect retrieval task that is more effective than a basic ret...", "weights": [[8, 14, 1.0], [127, 133, 1.0], [149, 158, 1.0], [200, 209, 1.0]]}, "2001.sigirconf_conference-2001.5": {"snippet": "...d in improving the effectiveness of information retrieval systems. Techniques such as relevance feedback, thesaural expansion, and pivoting all provide better quality responses to queries when tested ...", "weights": [[8, 17, 1.0], [22, 35, 1.0], [51, 60, 1.0]]}, "2011.sigirconf_conference-2011.13": {"snippet": "...nked retrieval\n\n\n ABSTRACTThere is a fundamental tradeoff between effectiveness and efficiency when designing retrieval models for large-scale document collections. Effectiveness tends to derive from ...", "weights": [[8, 17, 1.0], [69, 82, 1.0], [113, 122, 1.0], [168, 181, 1.0]]}, "N07-1028": {"snippet": "...tion retrieval systems are frequently required to handle long queries. Simply using all terms in the query or relying on the underlying retrieval model to appropriately weight terms often leads to ine...", "weights": [[8, 17, 1.0], [139, 148, 1.0]]}, "2004.cikm_conference-2004.47": {"snippet": "...tion retrieval systems. Each system demonstrated improved effectiveness with the use of blind feedback, in which the results of a preliminary retrieval step were used to augment the efficacy of a seco...", "weights": [[8, 17, 1.0], [32, 38, 1.0], [61, 74, 1.0], [145, 154, 1.0]]}, "2016.sigirconf_conference-2016.211": {"snippet": "...tion Retrieval systems have traditionally been evaluated in terms of efficiency and performance. These aspects of retrieval systems, whilst very important, do not cover a crucial aspect of the system,...", "weights": [[8, 17, 1.0], [117, 126, 1.0], [196, 202, 1.0]]}, "2006.ipm_journal-ir0volumeA42A3.2": {"snippet": "...ment retrieval behaviour. In particular relevance feedback query-expansion methods, which are often effective for improving electronic text retrieval, are observed to be less reliable for retrieval of...", "weights": [[8, 17, 1.0], [117, 126, 1.0], [143, 152, 1.0], [191, 200, 1.0]]}, "1972.ipm_journal-ir0volumeA8A5.1": {"snippet": "... the effectiveness of an on-line, natural language retrieval system", "weights": [[8, 21, 1.0], [54, 63, 1.0], [64, 70, 1.0]]}, "2021.mrqa-1.16": {"snippet": "...ense retrieval has been shown to be effective for Open Domain Question Answering, surpassing sparse retrieval methods like BM25. One such model, REALM, (Guu et al., 2020) is an end-to-end dense retrie...", "weights": [[8, 17, 1.0], [103, 112, 1.0], [197, 206, 1.0]]}, "2007.tois_journal-ir0volumeA26A1.4": {"snippet": "...ment retrieval\n\n\n Due to the existence of large numbers of legacy documents (such as old books and newspapers), improving retrieval effectiveness for OCR'ed documents continues to be an important prob...", "weights": [[8, 17, 1.0], [115, 124, 1.0], [125, 134, 1.0], [135, 148, 1.0]]}, "2011.ecir_conference-2011.27": {"snippet": "...s of Retrieval Effectiveness", "weights": [[8, 17, 1.0], [18, 31, 1.0]]}, "2004.ecir_conference-2004.21": {"snippet": "Improving Retrieval Effectiveness by Reranking Documents Based on Controlled Vocabulary", "weights": [[0, 9, 1.0], [10, 19, 1.0], [20, 33, 1.0]]}, "2008.tois_journal-ir0volumeA27A1.1": {"snippet": "...t of retrieval effectiveness\n\n\n A range of methods for measuring the effectiveness of information retrieval systems has been proposed. These are typically intended to provide a quantitative single-val...", "weights": [[8, 17, 1.0], [18, 31, 1.0], [72, 85, 1.0], [101, 110, 1.0]]}, "2013.ipm_journal-ir0volumeA49A1.7": {"snippet": "...tion retrieval\n\n\n a b s t r a c tThe Condorcet fusion is a distinctive fusion method and was found useful in information retrieval. Two basic requirements for the Condorcet fusion to improve retrieval...", "weights": [[8, 17, 1.0], [124, 133, 1.0], [194, 203, 1.0]]}, "1991.ipm_journal-ir0volumeA27A23.1": {"snippet": "... the effectiveness of retrieval algorithms", "weights": [[8, 21, 1.0], [25, 34, 1.0]]}, "2014.wwwjournals_journal-ir0volumeA17A4.15": {"snippet": "...ated retrieval, that is, the retrieval of blog entries which contain opinions about a topic. There has been a remarkable amount of work towards the improvement of the effectiveness of the opinion retr...", "weights": [[8, 17, 1.0], [32, 41, 1.0], [170, 183, 1.0], [199, 208, 1.0]]}, "2015.ipm_journal-ir0volumeA51A5.7": {"snippet": "... the effectiveness of a non-deterministic system with a deterministic or another non-deterministic system. In addition, we present a statistical best-practice that can be used to safely show how a non...", "weights": [[8, 21, 1.0], [45, 51, 1.0], [102, 108, 1.0]]}, "1973.jasis_journal-ir0volumeA24A2.1": {"snippet": "...e of retrieval effectiveness", "weights": [[8, 17, 1.0], [18, 31, 1.0]]}, "2005.ipm_journal-ir0volumeA41A1.7": {"snippet": "... the retrieval effectiveness of information retrieval systems, however the designing and the implementation of stemmers requires a laborious amount of effort due to the fact that documents and queries...", "weights": [[8, 17, 1.0], [18, 31, 1.0], [47, 56, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.20": {"snippet": "...ased retrieval.Directions for future work are using these concepts for communication between user and retrieval system, extending upon the translation models and extending CLIR-enhanced concept-based ...", "weights": [[8, 17, 1.0], [105, 114, 1.0], [115, 121, 1.0]]}, "1998.sigirconf_conference-98.15": {"snippet": "... the retrieval effectiveness of distributed information retrieval systems in realistic environments.We find that when a large number of collections are available, the retrieval effectiveness is signif...", "weights": [[8, 17, 1.0], [18, 31, 1.0], [59, 68, 1.0], [170, 179, 1.0], [180, 193, 1.0]]}, "1995.sigirconf_conference-95.30": {"snippet": "... the system, computes a score indicating how good those decisions were.Tune the system so that the expected effectiveness of its decisions will be the best possible.Have the system estimate the effect...", "weights": [[8, 14, 1.0], [83, 89, 1.0], [111, 124, 1.0], [177, 183, 1.0], [197, 210, 1.0]]}, "2001.sigirconf_workshop-2001w1.0": {"snippet": "...ment retrieval systems is not adversely affected by speech recognition errors. Even with error rates of around 40%, the effectiveness of an IR system falls less than 10%. The paper hypothesizes that t...", "weights": [[8, 17, 1.0], [123, 136, 1.0], [146, 152, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.15": {"snippet": "...ords retrieval model and the retrieval needs of the QA system. Bag-of-words IR retrieves documents matching a query, but the QA system really needs documents that contain answers. Through question ana...", "weights": [[8, 17, 1.0], [32, 41, 1.0], [58, 64, 1.0], [131, 137, 1.0]]}, "2003.trec_conference-2003.21": {"snippet": "...rove Retrieval Effectiveness", "weights": [[8, 17, 1.0], [18, 31, 1.0]]}, "1978.ipm_journal-ir0volumeA14A1.0": {"snippet": "...y of retrieval effectiveness measures", "weights": [[8, 17, 1.0], [18, 31, 1.0]]}, "2005.trec_conference-2005.11": {"snippet": "...rove Effectiveness and Robustness of Ad Hoc Information Retrieval\n\n\n Our participation in this year's robust track aims to: (1) test how to improve the effectiveness of IR (according to MAP) using dif...", "weights": [[8, 21, 1.0], [59, 68, 1.0], [155, 168, 1.0]]}, "2006.ipm_journal-ir0volumeA42A3.0": {"snippet": "...tion retrieval systems using data fusion\n\n\n AbstractMeasuring effectiveness of information retrieval (IR) systems is essential for research and development and for monitoring search quality in dynamic...", "weights": [[8, 17, 1.0], [65, 78, 1.0], [94, 103, 1.0]]}, "2005.ipm_journal-ir0volumeA41A5.11": {"snippet": "... for improving web retrieval effectiveness\n\n\n AbstractThis paper talks about several schemes for improving retrieval effectiveness that can be used in the named page finding tasks of web information r...", "weights": [[8, 17, 1.0], [22, 31, 1.0], [32, 45, 1.0], [100, 109, 1.0], [110, 119, 1.0], [120, 133, 1.0], [202, 211, 1.0]]}, "2007.sigirconf_conference-2007.212": {"snippet": "...ween system effectiveness and users' performance. People learn to adapt to a system regardless to its effectiveness. This study recommends that a combination of measures (e.g. system effectiveness, us...", "weights": [[8, 14, 1.0], [15, 28, 1.0], [80, 86, 1.0], [105, 118, 1.0], [179, 185, 1.0], [186, 199, 1.0]]}, "2003.sigirconf_conference-2003.49": {"snippet": "... and Retrieval]: Retrieval Models.General Terms: Theory, Experimentation. Keywords: Ranking Retrieval Systems.\nINTRODUCTIONSoboroff, Nicholas and Cahan [2] recently proposed a method for evaluating re...", "weights": [[8, 17, 1.0], [20, 29, 1.0], [95, 104, 1.0], [201, 210, 1.0]]}, "2000.sigirconf_conference-2000.7": {"snippet": "... the effectiveness of query types, based on combinations of query structures and expansion, in retrieving documents of various degrees of relevance. The test was run with a best match retrieval system...", "weights": [[8, 21, 1.0], [187, 196, 1.0], [197, 203, 1.0]]}, "1988.jasis_journal-ir0volumeA39A2.0": {"snippet": "... the effectiveness of full-text retrieval. I. On the effectiveness of full-text retrieval", "weights": [[8, 21, 1.0], [35, 44, 1.0], [56, 69, 1.0], [83, 92, 1.0]]}, "2018.wsdm_conference-2018.47": {"snippet": "...zing Retrieval Systems\n\n\n ABSTRACTThe effectiveness of information retrieval systems heavily depends on a large number of hyperparameters that need to be tuned. Hyperparameters range from the choice o...", "weights": [[8, 17, 1.0], [41, 54, 1.0], [70, 79, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A1.9": {"snippet": "... the effectiveness of queries and retrieval systems", "weights": [[8, 21, 1.0], [37, 46, 1.0]]}, "2008.sigirconf_conference-2008.127": {"snippet": "... web retrieval\n\n\n ABSTRACTDocument prior features, such as Pagerank and URL depth, can improve the retrieval effectiveness of Web Information Retrieval (IR) systems. However, not all queries equally b...", "weights": [[8, 17, 1.0], [102, 111, 1.0], [112, 125, 1.0], [145, 154, 1.0]]}, "2016.ntcir_conference-2016.90": {"snippet": "...tent Retrieval (SCR) subtask. We investigated the use of multiple ASR hypotheses (words) and subword units (syllables) for improving retrieval effectiveness. We also compared the retrieval effectivene...", "weights": [[8, 17, 1.0], [126, 135, 1.0], [136, 145, 1.0], [146, 159, 1.0], [182, 191, 1.0], [192, 205, 1.0]]}, "1997.sigirconf_conference-97.36": {"snippet": "...cale retrieval context, or whether inexperienced users can achieve this effectiveness in practice. These experiments address these two questions. A small but significant improvement in potential retri...", "weights": [[8, 17, 1.0], [75, 88, 1.0], [198, 207, 1.0]]}, "2018.sigirjournals_journal-ir0volumeA52A2.23": {"snippet": "...tion Retrieval (IR), accurately interpreting the result of a system in terms of user satisfaction is fundamental to push the research in the correct direction. Therefore, measuring systems effectivene...", "weights": [[8, 17, 1.0], [64, 70, 1.0], [192, 205, 1.0]]}, "2007.ipm_journal-ir0volumeA43A2.9": {"snippet": "...to a retrieval system and obtains no relevant documents, he or she is bound to feel frustrated. We call these weak queries and retrievals. Improving their effectiveness is an important issue for ad ho...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [142, 151, 1.0], [158, 171, 1.0]]}, "2004.clef_workshop-2004.81": {"snippet": "...e to Retrieval Effectiveness?", "weights": [[8, 17, 1.0], [18, 31, 1.0]]}, "2018.ipm_journal-ir0volumeA54A2.1": {"snippet": "...meta-system. Even simple ensemble techniques, when used in an informed manner, prove to be very effective in improving the overall performance and consistency of summarization systems. A statistically...", "weights": [[8, 14, 1.0], [112, 121, 1.0]]}, "1999.ntcir_workshop-1999.31": {"snippet": "...ndex System at NTCIR1: Information Retrieval using Dependency Relationship between Words\n\n\n It is difficult to improve retrieval effectiveness using only keyword-based retrieval, the major method in d...", "weights": [[8, 14, 1.0], [38, 47, 1.0], [122, 131, 1.0], [132, 145, 1.0], [171, 180, 1.0]]}}} -{"dataset": "ir-lab-sose-2024/ir-acl-anthology-20240504-training", "qid": "2", "default_text": "machine learning language identification", "runs": [{"name": "ir-lab-sose-2024/tira-ir-starter/BM25 (tira-ir-starter-pyterrier)", "P@10": 0.2, "R@100": 0.75, "nDCG@10": 0.158, "Judged@10": 0.3, "relevance": ["0", "U", "U", "1", "U", "U", "U", "U", "U", "1", "1"], "docs": [{"doc_id": "2019.fire_conference-2019w.41", "doc_id_to_offset": {"start": 162683120, "end": 162684701}}, {"doc_id": "D07-1057", "doc_id_to_offset": {"start": 89234300, "end": 89235854}}, {"doc_id": "W12-5608", 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We propose a two stage approach for word-level langua...", "weights": [[8, 16, 1.0], [79, 87, 1.0], [197, 205, 1.0]]}, "2018.tist_journal-ir0volumeA9A5.0": {"snippet": "Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments\n\n\n Eliminating the negative effect of non-stationary environmental noise is a long-standing research top...", "weights": [[5, 13, 1.0]]}, "2017.fire_conference-2017w.30": {"snippet": "...tive Language Identification using Support Vector Machines and Ensemble approach\n\n\n This paper describes the systems submitted by our team for Indian Native Language Identification (INLI) task held in...", "weights": [[8, 16, 1.0], [17, 31, 1.0], [160, 168, 1.0], [169, 183, 1.0]]}, "2013.tois_journal-ir0volumeA31A2.1": {"snippet": "...ious machine-learning-based NER systems are domain-specific, which implies that they may only perform well on some specific domains (e.g., Newswire) but tend to adapt poorly to other related but diffe...", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "S19-2107": {"snippet": "...sive Language Identification using Traditional Machine Learning and Deep Learning approaches\n\n\n In this paper, the used methods and the results obtained by our team, entitled Emad, on the OffensEval 2...", "weights": [[8, 16, 1.0], [17, 31, 1.0], [50, 57, 1.0], [58, 66, 1.0], [76, 84, 1.0]]}, "L12-1354": {"snippet": "... for Machine Learning Approach to Authorship Identification", "weights": [[8, 15, 1.0], [16, 24, 1.0], [48, 62, 1.0]]}, "W19-1420": {"snippet": "...ting Machine Learning Methods for Language and Dialect Identification of Cuneiform Texts\n\n\n Identification of the languages written using cuneiform symbols is a difficult task due to the lack of resou...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [37, 45, 1.0], [58, 72, 1.0], [95, 109, 1.0]]}, "N19-5005": {"snippet": "Language Learning and Processing in People and Machines", "weights": [[0, 8, 1.0], [9, 17, 1.0]]}, "2020.eamt-1.69": {"snippet": "...ect: Machine Translation impact on language learning", "weights": [[8, 15, 1.0], [38, 46, 1.0], [47, 55, 1.0]]}, "L02-1094": {"snippet": "...n of Machine Learning Methods for Natural Language Processing Tasks", "weights": [[8, 15, 1.0], [16, 24, 1.0], [45, 53, 1.0]]}, "2020.sigirjournals_journal-ir0volumeA54A1.14": {"snippet": "Machine learning applied in natural language processing", "weights": [[0, 7, 1.0], [8, 16, 1.0], [36, 44, 1.0]]}, "S19-2130": {"snippet": "...sive Language Identification in Social Media using Traditional and Deep Machine Learning Approaches\n\n\n Offensive language identification (OLI) in user generated text is automatic detection of any prof...", "weights": [[8, 16, 1.0], [17, 31, 1.0], [75, 82, 1.0], [83, 91, 1.0], [116, 124, 1.0], [125, 139, 1.0]]}, "W13-1735": {"snippet": "...tive Language\n\n\n This paper presents our approach to the 2013 Native Language Identification shared task, which is based on machine learning methods that work at the character level. More precisely, w...", "weights": [[8, 16, 1.0], [72, 80, 1.0], [81, 95, 1.0], [127, 134, 1.0], [135, 143, 1.0]]}, "2017.fire_conference-2017w.34": {"snippet": "...tive Language Identification using SVM with SGD Training\n\n\n Native Language Identification (NLI), as a variant of Language Identification task, focuses on determining an author's native language, base...", "weights": [[8, 16, 1.0], [17, 31, 1.0], [70, 78, 1.0], [79, 93, 1.0], [117, 125, 1.0], [126, 140, 1.0], [189, 197, 1.0]]}, "2008.ipm_journal-ir0volumeA44A3.6": {"snippet": "...mmon language modeling approach assumes the data D is generated from a mixture of several language models. The core problem is to find the maximum likelihood estimation of one language model mixture, ...", "weights": [[8, 16, 1.0], [93, 101, 1.0], [179, 187, 1.0]]}, "2009.ipm_journal-ir0volumeA45A1.0": {"snippet": "A language modeling framework for expert finding\n\n\n a b s t r a c tStatistical language models have been successfully applied to many information retrieval tasks, including expert finding: the process...", "weights": [[2, 10, 1.0], [79, 87, 1.0]]}, "2021.wanlp-1.34": {"snippet": "Machine Learning-Based Approach for {A}rabic Dialect Identification\n\n\n This paper describes our systems submitted to the Second Nuanced Arabic Dialect Identification Shared Task (NADI 2021). Dialect i...", "weights": [[0, 7, 1.0], [8, 16, 1.0], [53, 67, 1.0], [151, 165, 1.0], [199, 213, 1.0]]}, "2006.ipm_journal-ir0volumeA42A4.7": {"snippet": "...sing machine transliteration. In this paper, we propose a method for improving machine transliteration using an ensemble of three different transliteration models. Because one transliteration model al...", "weights": [[8, 15, 1.0], [82, 89, 1.0]]}, "2018.fire_conference-2018w.10": {"snippet": "...tive Language Identification INLI@FIRE2018\n\n\n Native Language Identification is the process of determining the language native to the author from the written text. We have proposed a system that uses ...", "weights": [[8, 16, 1.0], [17, 31, 1.0], [56, 64, 1.0], [65, 79, 1.0], [114, 122, 1.0]]}, "2020.semeval-1.287": {"snippet": "...sive Language Identification in {E}nglish, {D}anish, {G}reek Using {BERT} and Machine Learning Approach\n\n\n Offensive language identification is to detect the hurtful tweets, derogatory comments, swear...", "weights": [[8, 16, 1.0], [17, 31, 1.0], [81, 88, 1.0], [89, 97, 1.0], [120, 128, 1.0], [129, 143, 1.0]]}, "W14-5151": {"snippet": "...evel Language Identification\n\n\n Language identification is a necessary prerequisite for processing any user generated text, where the language is unknown. It becomes even more challenging when the tex...", "weights": [[8, 16, 1.0], [17, 31, 1.0], [35, 43, 1.0], [44, 58, 1.0], [137, 145, 1.0]]}, "W18-4414": {"snippet": "...sive Language Identification Using Word Embeddings and Sentiment Features\n\n\n This paper describes our participation in the First Shared Task on Aggression Identification. The method proposed relies on...", "weights": [[8, 16, 1.0], [17, 31, 1.0], [158, 172, 1.0]]}, "2018.ipm_journal-ir0volumeA54A6.16": {"snippet": "...ural language models Deep learning A B S T R A C T Automatic text classification is the task of organizing documents into pre-determined classes, generally using machine learning algorithms. Generally...", "weights": [[8, 16, 1.0], [29, 37, 1.0], [165, 172, 1.0], [173, 181, 1.0]]}, "2014.fire_conference-2014.18": {"snippet": "...sing Machine Learning\n\n\n ABSTRACTThis paper presents the method of Morpheme Extraction and lemmatization for Tamil language in Morpheme Extraction Task (MET) of FIRE-2014. Tamil is a morphologically r...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [118, 126, 1.0]]}, "2014.fire_conference-2014.9": {"snippet": "Machine Learning Approach for Language Identification & Transliteration\n\n\n ABSTRACTIn this paper, we describe the system that we developed as part of our participation to the FIRE-2014 Shared Task on ...", "weights": [[0, 7, 1.0], [8, 16, 1.0], [30, 38, 1.0], [39, 53, 1.0]]}, "W19-6301": {"snippet": "...sing machine learning\n\n\n Predicting the knowledge of language learners is crucial for personalized interactions in any intelligent tutoring system for language learning. This study adopts a machine le...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [56, 64, 1.0], [154, 162, 1.0], [163, 171, 1.0], [193, 200, 1.0], [201, 209, 1.0]]}, "2017.fire_conference-2017w.36": {"snippet": "...tive Language Identification\n\n\n Native Language Identification has played an important role in forensics primarily for author profiling and identification. In this work, we discuss our approach to the...", "weights": [[8, 16, 1.0], [17, 31, 1.0], [42, 50, 1.0], [51, 65, 1.0], [143, 157, 1.0]]}, "W18-1203": {"snippet": "...ural language model (LM), and data bootstrapping to overcome the problem of unbalanced limited resources. As a test, we explore the task of language identification in mixed-language short non-edited t...", "weights": [[8, 16, 1.0], [143, 151, 1.0], [152, 166, 1.0], [176, 184, 1.0]]}, "2018.fire_conference-2018w.11": {"snippet": "A Machine Learning Approach to Indian Native Language Identification\n\n\n NLI (Native Language Identification) determines the native language of the non-native users using their writings in a foreign la...", "weights": [[2, 9, 1.0], [10, 18, 1.0], [45, 53, 1.0], [54, 68, 1.0], [84, 92, 1.0], [93, 107, 1.0], [131, 139, 1.0], [198, 206, 1.0]]}, "1991.riao_conference-1991.48": {"snippet": "Machine learning principles in application to a transportable natural language database interface", "weights": [[0, 7, 1.0], [8, 16, 1.0], [70, 78, 1.0]]}, "I11-1062": {"snippet": "... for Language Identification\n\n\n We show that transductive (cross-domain) learning is an important consideration in building a general-purpose language identification system, and develop a feature sele...", "weights": [[8, 16, 1.0], [17, 31, 1.0], [76, 84, 1.0], [145, 153, 1.0], [154, 168, 1.0]]}, "2015.fire_workshop-2015w.7": {"snippet": "...ased machine learning framework was used for labeling the individual words with their corresponding language labels. We used a dictionary based approach for language identification. We also took into ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [103, 111, 1.0], [160, 168, 1.0], [169, 183, 1.0]]}, "2014.airs_conference-2014.24": {"snippet": "...d on machine learning approaches that extract nationalities, weapons, and crime locations in online crime documents. This study also collected a new corpus of crime and manually labeled them. A machin...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [197, 204, 1.0]]}, "2019.fire_conference-2019w.45": {"snippet": "...9: A Machine Learning Framework for Hate Speech and Offensive Language Detection\n\n\n In this paper, we describe the system submitted by our team for Hate Speech and Offensive Content Identification in ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [65, 73, 1.0], [185, 199, 1.0]]}, "2007.ipm_journal-ir0volumeA43A5.3": {"snippet": "...eral machine learning algorithms for cross-lan7guage patent retrieval and classification. In comparison with most of other studies involving machine learning for cross-language information retrieval, ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [144, 151, 1.0], [152, 160, 1.0], [171, 179, 1.0]]}, "W05-0401": {"snippet": "...ovel Machine Learning Approach for the Identification of Named Entity Relations\n\n\n In this paper, a novel machine learning approach for the identification of named entity relations (NERs) called posit...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [42, 56, 1.0], [109, 116, 1.0], [117, 125, 1.0], [143, 157, 1.0]]}, "2020.semeval-1.289": {"snippet": "...d of machine learning and deep learning models for all five languages. Three machine learning and four deep learning models were used in the ensemble. In the OffensEval-2020 competition our model achi...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [34, 42, 1.0], [80, 87, 1.0], [88, 96, 1.0], [111, 119, 1.0]]}, "P01-1020": {"snippet": "A Machine Learning Approach to the Automatic Evaluation of Machine Translation\n\n\n We present a machine learning approach to evaluating the wellformedness of output of a machine translation system, usi...", "weights": [[2, 9, 1.0], [10, 18, 1.0], [59, 66, 1.0], [95, 102, 1.0], [103, 111, 1.0], [169, 176, 1.0]]}, "D07-1057": {"snippet": "Identification and Resolution of {C}hinese Zero Pronouns: A Machine Learning Approach\n\n\n In this paper, we present a machine learning approach to the identification and resolution of Chinese anaphoric...", "weights": [[0, 14, 1.0], [60, 67, 1.0], [68, 76, 1.0], [117, 124, 1.0], [125, 133, 1.0], [150, 164, 1.0]]}, "W16-5818": {"snippet": "...hing language identification using Subword Information Enriched Word Vectors\n\n\n Codeswitching is a widely observed phenomenon among bilingual speakers. By combining subword information enriched word v...", "weights": [[8, 16, 1.0], [17, 31, 1.0]]}, "2019.fire_conference-2019w.42": {"snippet": "...019: Identification of Hate Speech using Machine Learning and Deep Learning approaches for social media post\n\n\n This paper describes the research that our team, DA Master, did on the shared task HASOC...", "weights": [[8, 22, 1.0], [44, 51, 1.0], [52, 60, 1.0], [70, 78, 1.0]]}, "2019.fire_conference-2019w.36": {"snippet": "...sive language on Twitter in English, German and Hindi languages. As a part of this task, we (team A3-108) submitted different machine learning and neural network based models for all the languages. Ou...", "weights": [[8, 16, 1.0], [129, 136, 1.0], [137, 145, 1.0]]}, "2016.ipm_journal-ir0volumeA52A4.0": {"snippet": "...m of language identification of noisy texts, which could represent the primary step of many natural language processing or information retrieval tasks. Language identification is the task of automatic...", "weights": [[8, 16, 1.0], [17, 31, 1.0], [103, 111, 1.0], [155, 163, 1.0], [164, 178, 1.0]]}, "W17-1301": {"snippet": "Identification of Languages in {A}lgerian {A}rabic Multilingual Documents\n\n\n This paper presents a language identification system designed to detect the language of each word, in its context, in a mul...", "weights": [[0, 14, 1.0], [99, 107, 1.0], [108, 122, 1.0], [153, 161, 1.0]]}, "W13-1734": {"snippet": "...tive Language Identification using large scale lexical features\n\n\n This paper describes an effort to perform Native Language Identification (NLI) using machine learning on a large amount of lexical fe...", "weights": [[8, 16, 1.0], [17, 31, 1.0], [119, 127, 1.0], [128, 142, 1.0], [155, 162, 1.0], [163, 171, 1.0]]}, "R15-1040": {"snippet": "...tive Language Identification Using Language Modeling\n\n\n This paper reports on the task of Native Language Identification (NLI). We developed a machine learning system to identify the native language o...", "weights": [[8, 16, 1.0], [17, 31, 1.0], [38, 46, 1.0], [100, 108, 1.0], [109, 123, 1.0], [146, 153, 1.0], [154, 162, 1.0], [193, 201, 1.0]]}, "U09-1008": {"snippet": "...tive Language Identification\n\n\n Attempts to profile authors based on their characteristics, including native language, have drawn attention in recent years, via several approaches using machine learni...", "weights": [[8, 16, 1.0], [17, 31, 1.0], [112, 120, 1.0], [189, 196, 1.0], [197, 205, 1.0]]}, "2020.coling-main.159": {"snippet": "...tive Language Identification\n\n\n Native language identification (NLI) -identifying the native language (L1) of a person based on his/her writing in the second language (L2) -is useful for a variety of ...", "weights": [[8, 16, 1.0], [17, 31, 1.0], [42, 50, 1.0], [51, 65, 1.0], [96, 104, 1.0], [161, 169, 1.0]]}, "E17-1087": {"snippet": "...gual Language Identification on Character Window\n\n\n In language identification, a common first step in natural language processing, we want to automatically determine the language of some input text. ...", "weights": [[8, 16, 1.0], [17, 31, 1.0], [58, 66, 1.0], [67, 81, 1.0], [114, 122, 1.0], [174, 182, 1.0]]}, "2020.semeval-1.264": {"snippet": "... 12: Machine Learning and Deep Learning Methods for Offensive Language Identification\n\n\n The paper describes systems that our team IRLab DAIICT employed for shared task OffensEval 2020: Multilingual O...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [34, 42, 1.0], [65, 73, 1.0], [74, 88, 1.0]]}, "2014.fire_conference-2014.8": {"snippet": "...ased machine learning model and post-processing heuristics are employed for the WLL identification task. 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Pervasiv...", "weights": []}, "2020.clef_conference-2020w.135": {"snippet": "...s in social media.", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2017.sigirconf_conference-2017.232": {"snippet": "... and Social Media\n\n\n ABSTRACTPeople regularly use web search and social media to investigate health related issues.is type of Internet data might contain misinformation i.e. incorrect information whic...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [68, 74, 1.0], [75, 80, 1.0]]}, "2015.wwwconf_conference-2015c.131": {"snippet": "...from Social Media Streams\n\n\n ABSTRACTMy proposed research addresses fundamental deficiencies in social media-based event detection by discovering highimpact moments and evaluating their credibility ra...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [99, 105, 1.0], [106, 111, 1.0]]}, "2018.wwwconf_conference-2018c.138": {"snippet": "...sing Social Media Messages in Mass Emergency: Survey Summary\n\n\n ABSTRACTMillions of people use social media to share information during disasters and mass emergencies. Information available on social ...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [98, 104, 1.0], [105, 110, 1.0], [196, 202, 1.0]]}, "2020.chiir_conference-2020.49": {"snippet": "Detecting Critical Responses from Deliberate Self-harm Videos on YouTube", "weights": []}, "2015.wwwconf_conference-2015c.125": {"snippet": "...mely social spams. While it is intuitive for everyone to follow legitimate users, recent studies show that both legitimate users and spammers follow spammers for different reasons. Evidence of users s...", "weights": [[8, 14, 1.0]]}, "2020.wwwconf_conference-2020.216": {"snippet": "...s in Social Media", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2014.sigirconf_conference-2014.185": {"snippet": "...y of social media to automatically detect road hazards. We conduct experiments on a testbed of tweets discussing road conditions and the initial results demonstrate the effectiveness of our approach.", "weights": [[8, 14, 1.0], [15, 20, 1.0], [38, 44, 1.0]]}, "2019.clef_conference-2019w.170": {"snippet": "...ting Social Media users, namely Reddit, who may be under the risk of Anorexia, self-harm, and depression. We participated in both tasks of early risk detection of Anorexia and self-harm. Our predictio...", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2018.wwwconf_conference-2018.13": {"snippet": "...s in Social Media\n\n\n ABSTRACTMillions of users share their experiences on social media sites, such as Twitter, which in turn generate valuable data for public health monitoring, digital epidemiology, ...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [77, 83, 1.0], [84, 89, 1.0]]}, "2017.wwwconf_conference-2017c.3": {"snippet": "... the social media era. People are willing to share their selfies in various social media platforms such as Facebook, Instagram and Flicker. The popularity of selfie have caught researchers' attention,...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [79, 85, 1.0], [86, 91, 1.0]]}, "2019.clef_conference-2019w.19": {"snippet": "...m on Social Media\n\n\n Three tasks are proposed at CLEF eRisk-2019 for predicting mental disorder using users posts on Reddit. Two tasks (T1 and T2) focus on early risk detection of signs of anorexia an...", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2019.clef_conference-2019w.1": {"snippet": "A Baseline Approach for Early Detection of Signs of Anorexia and Self-harm in Reddit Posts\n\n\n This paper describes the systems developed by the BiTeM team for the CLEF eRisk Task 1 and 2, 2019. The go...", "weights": []}, "2020.clef_conference-2020w.60": {"snippet": "...hich social media users are at risk of self-harming, with precision up to 91.3% for Task 1. Given these promising results, it will be interesting to further refine the training regime, classifier and ...", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2020.ipm_journal-ir0volumeA57A3.2": {"snippet": "...n on social media", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2020.tweb_journal-ir0volumeA14A4.3": {"snippet": "...n of Social Media Hoaxes at Scale", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2021.wwwconf_conference-2021c.39": {"snippet": "...with Social Media Data", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2018.tist_journal-ir0volumeA9A5.1": {"snippet": "...h of social media, massive misinformation is also spreading widely on social media, e.g., Weibo and Twitter, and brings negative effects to human life. Today, automatic misinformation identification h...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [73, 79, 1.0], [80, 85, 1.0]]}, "2015.jasis_journal-ir0volumeA66A6.14": {"snippet": "... for social media", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2020.jasis_journal-ir0volumeA71A1.3": {"snippet": "...sing social media", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2019.wwwconf_conference-2019c.14": {"snippet": "...g in Social Media", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2020.clef_conference-2020w.116": {"snippet": "...e to detect these individuals through their content on social media. Early detection is crucial for mental health problems, and for this purpose a shared task named eRisk was proposed. This paper desc...", "weights": [[8, 14, 1.0], [58, 64, 1.0], [65, 70, 1.0]]}, "2017.wwwjournals_journal-ir0volumeA20A2.11": {"snippet": "... for social user profiling\n\n\n Abstract Traditionally, research about social user profiling assumes that users share some similar interests with their followees. However, it lacks the studies on what t...", "weights": [[8, 14, 1.0], [72, 78, 1.0]]}, "2017.tist_journal-ir0volumeA8A6.11": {"snippet": "...mely social spams. Although it is intuitive for everyone to follow legitimate users, recent studies show that both legitimate users and spammers follow spammers for different reasons. Evidence of user...", "weights": [[8, 14, 1.0]]}, "W17-3108": {"snippet": "...s on social media may reveal themselves to be in various states of crisis (e.g. suicide, self-harm, abuse, or eating disorders). Detecting crisis from social media text automatically and accurately ca...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [154, 160, 1.0], [161, 166, 1.0]]}, "2016.cikm_workshop-2016cybersafety.1": {"snippet": "...s to detect bad actors in social media. More specifically, we will describe both general methods that are platform independent or valid for any malicious user, and methods specific to a particular soc...", "weights": [[8, 14, 1.0], [29, 35, 1.0], [36, 41, 1.0], [200, 206, 1.0]]}, "W18-4421": {"snippet": "...cale social media monitoring and early detection of undesired situations. This paper presents the Isistanitos's approach for detecting aggressive content in multiple social media sites. The approach i...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [169, 175, 1.0], [176, 181, 1.0]]}, "2018.wwwconf_conference-2018c.181": {"snippet": "...e of social media can be leveraged to identify stress and relaxation. In this PhD research, we introduce an improved method to detect expressions of stress and relaxation in social media content. It u...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [130, 136, 1.0], [177, 183, 1.0], [184, 189, 1.0]]}, "2019.ecir_conference-20192.48": {"snippet": "...d in Social Media platforms and other online communities an easier way to reveal their minds. The textual content produced by such users proved to be handful in evaluating mental health and detecting ...", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2020.clef_conference-2020w.90": {"snippet": "...s in social media.", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2019.clef_conference-2019w.171": {"snippet": "...n in Social Media\n\n\n In this paper we describe the participation of our research group at the CLEF eRisk 2019. The eRisk goal is the early detection of atrisk people by means of machine learning techn...", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "R17-1062": {"snippet": "...h in Social Media", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2021.findings-acl.246": {"snippet": "...n in social media, the use of Internet memes has emerged as a powerful means to convey political, psychological, and socio-cultural opinions. 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One of the high inflected words in the language...", "weights": [[8, 14, 1.0], [42, 50, 1.0], [195, 204, 1.0]]}, "2012.cikm_conference-2012.349": {"snippet": "... the Arabic microblogs retrieval. We illustrate some of the challenges associated with Arabic microblog retrieval, which mainly stem from the use of different Arabic dialects that vary in lexical sele...", "weights": [[8, 14, 1.0], [90, 96, 1.0], [162, 168, 1.0]]}, "1998.tois_journal-ir0volumeA16A1.2": {"snippet": "...ased Stemming Using Cooccurrence of Word Variants\n\n\n Stemming is used in many information retrieval (IR) systems to reduce variant word forms to common roots. It is one of the simplest applications of...", "weights": [[8, 16, 1.0], [56, 64, 1.0]]}, "C08-2027": {"snippet": "...s\n\n\n Arabic morphological analysers and stemming algorithms have become a popular area of research. Many computational linguists have designed and developed algorithms to solve the problem of morpholo...", "weights": [[8, 14, 1.0], [43, 51, 1.0]]}, "2008.clef_workshop-2008.10": {"snippet": "...sing Stemming and Part of Speech Tagging\n\n\n Abstract. With the emergence of vast resources of information, it is necessary to develop methods that retrieve the most relevant information according to n...", "weights": [[8, 16, 1.0]]}, "2006.bcs-1.6": {"snippet": "... new stemming technique that tries to determine the stem of a word representing the semantic core of this word according to Arabic morphology. This method is compared to a commonly used light stemming...", "weights": [[8, 16, 1.0], [127, 133, 1.0], [195, 203, 1.0]]}, "2013.tois_journal-ir0volumeA31A4.1": {"snippet": "...ased Stemming\n\n\n Stemming is a widely used technique in information retrieval systems to address the vocabulary mismatch problem arising out of morphological phenomena. The major shortcoming of the co...", "weights": [[8, 16, 1.0], [20, 28, 1.0]]}, "2011.ijirr_journal-ir0volumeA1A3.3": {"snippet": "...t of Stemming on Arabic Text Classification: An Empirical Study", "weights": [[8, 16, 1.0], [20, 26, 1.0]]}, "2006.airs_conference-2006.25": {"snippet": "... for stemming for an open class of languages. Since it does not rely on existing large data collections or other linguistic resources than raw text it is especially attractive for low-density language...", "weights": [[8, 16, 1.0], [38, 47, 1.0], [195, 204, 1.0]]}, "P12-2043": {"snippet": "...e to Arabic's morphological complexity, Arabic retrieval benefits greatly from morphological analysis -particularly stemming. However, the best known stemming does not handle linguistic phenomena such...", "weights": [[8, 14, 1.0], [43, 49, 1.0], [119, 127, 1.0], [153, 161, 1.0]]}, "W04-1605": {"snippet": "...g in Arabic requires an efficient method for matching stems with roots and vice versa. Such mapping is complicated by the hundreds of manifestations of the same root. An algorithm based on the generat...", "weights": [[8, 14, 1.0]]}, "2004.sigirconf_conference-2004.112": {"snippet": "... for arabic information retrieval\n\n\n ABSTRACTDue to the high number of inflectional variations of Arabic words, empirical results suggest that stemming is essential for Arabic information retrieval. H...", "weights": [[8, 14, 1.0], [101, 107, 1.0], [146, 154, 1.0], [172, 178, 1.0]]}, "2009.ipm_journal-ir0volumeA45A6.6": {"snippet": "... and stemming approaches for the Czech language\n\n\n a b s t r a c tThis paper describes and evaluates various stemming and indexing strategies for the Czech language. Based on Czech test-collection, we...", "weights": [[8, 16, 1.0], [112, 120, 1.0]]}, "2000.spire_conference-2000.16": {"snippet": "...Word Stemming Algorithm for the Spanish Language", "weights": [[8, 16, 1.0]]}, "2010.airs_conference-2010.27": {"snippet": "...ased Arabic Stemming Using N-Grams\n\n\n Abstract. In languages with high word inflation such as Arabic, stemming improves text retrieval performance by reducing words variants. We propose a change in th...", "weights": [[8, 14, 1.0], [15, 23, 1.0], [54, 63, 1.0], [97, 103, 1.0], [105, 113, 1.0]]}, "Y10-1068": {"snippet": "...s in Arabic language can be attached to a stem or to each other without orthographic marks such as an apostrophe. In this paper we present a statistical study of clitics and its effect in Arabic langu...", "weights": [[8, 14, 1.0], [191, 197, 1.0]]}, "2005.ipm_journal-ir0volumeA41A3.3": {"snippet": "...hile stemming is useful normally, with a very large parallel corpus for Arabic-English, stemming hurt performance in our empirical studies with Arabic, a highly inflected language.", "weights": [[8, 16, 1.0], [75, 81, 1.0], [91, 99, 1.0], [147, 153, 1.0]]}, "1999.jasis_journal-ir0volumeA50A6.4": {"snippet": "Stemming Methodologies Over Individual Query Words for an Arabic Information Retrieval System", "weights": [[0, 8, 1.0], [58, 64, 1.0]]}, "C98-1018": {"snippet": "...cale Arabic morphological analyzer which is available for testing on the Internet. introduction Most formal analyses of Semitic languages, including Arabic. deDnd the reality of abstract, unpronouncea...", "weights": [[8, 14, 1.0], [131, 140, 1.0], [152, 158, 1.0]]}, "2005.spire_conference-2005.23": {"snippet": "Stemming Arabic Conjunctions and Prepositions", "weights": [[0, 8, 1.0], [9, 15, 1.0]]}, "W05-0709": {"snippet": "...ical Stemming on {A}rabic Mention Detection and Coreference Resolution\n\n\n Arabic presents an interesting challenge to natural language processing, being a highly inflected and agglutinative language. ...", "weights": [[8, 16, 1.0], [77, 83, 1.0]]}, "2008.cikm_workshop-2008inews.2": {"snippet": "...tive Arabic stemming algorithm that can reduce stemming errors in addition to decreasing computational time and data storage.The novelty of the work arises from the use of neglected Arabic stop-words....", "weights": [[8, 14, 1.0], [15, 23, 1.0], [50, 58, 1.0], [185, 191, 1.0]]}, "2019.ipm_journal-ir0volumeA56A2.1": {"snippet": "... for Arabic dialects (survey)\n\n\n a b s t r a c tArabic dialects also called colloquial Arabic or vernaculars are spoken varieties of Standard Arabic. These dialects have mixed form with many variation...", "weights": [[8, 14, 1.0], [90, 96, 1.0], [145, 151, 1.0]]}, "2002.sigirconf_conference-2002.36": {"snippet": "... for Arabic retrieval\n\n\n ABSTRACTThis work evaluates a few search strategies for Arabic monolingual and cross-lingual retrieval, using the TREC Arabic corpus as the test-bed. The release by NIST in 20...", "weights": [[8, 14, 1.0], [84, 90, 1.0], [147, 153, 1.0]]}, "1990.ipm_journal-ir0volumeA26A2.8": {"snippet": "...n of Arabic text", "weights": [[8, 14, 1.0]]}, "2003.mtsummit-semit.5": {"snippet": "...with Arabic as a source or target language, and focuses on lexical databases. The non-concatenative nature of Arabic morphology, the complex structure of Arabic word-forms, and the general use of vowe...", "weights": [[8, 14, 1.0], [113, 119, 1.0], [157, 163, 1.0]]}, "W04-1616": {"snippet": "...s to Arabic stemming fail to provide effective and accurate equivalence classes when applied to a text like the Qur'an written in Classical Arabic. Therefore, I propose a new stemming approach based o...", "weights": [[8, 14, 1.0], [15, 23, 1.0], [143, 149, 1.0], [178, 186, 1.0]]}, "2016.tois_journal-ir0volumeA35A1.5": {"snippet": "...ther languages, little effort was directed towards QA systems for Arabic. Due to the difficulty of handling why-questions, most Arabic QA systems tend to ignore it. In this article, we specifically ad...", "weights": [[8, 17, 1.0], [69, 75, 1.0], [131, 137, 1.0]]}, "2014.cikm_conference-2014.185": {"snippet": "...mple Arabic Stemmer\n\n\n ABSTRACTWe propose a root stemmer for the Modern Standard Arabic (MSA) language in an attempt to enhance the performance of Arabic Information Retrieval (AIR). The new Simple Ar...", "weights": [[8, 14, 1.0], [84, 90, 1.0], [150, 156, 1.0], [201, 207, 1.0]]}, "W06-1631": {"snippet": "...h as stemming should not be applied indiscriminately to all words in a collection, and so before any stemming, foreign words need to be identified. In this paper, we investigate three approaches for t...", "weights": [[8, 16, 1.0], [104, 112, 1.0]]}, "P03-1050": {"snippet": "...abic Stemming Using a Parallel Corpus\n\n\n This paper presents an unsupervised learning approach to building a non-English (Arabic) stemmer. The stemming model is based on statistical machine translatio...", "weights": [[8, 16, 1.0], [125, 131, 1.0], [146, 154, 1.0]]}, "2008.sigirconf_workshop-2008and.16": {"snippet": "... the Arabic Language. The new lemmatizer proposed here is a part of a comprehensive Arabic tokenization system, with a stop words list exceeding 2200 Arabic words. Currently, there are two Arabic lead...", "weights": [[8, 14, 1.0], [87, 93, 1.0], [153, 159, 1.0], [192, 198, 1.0]]}, "2001.spire_conference-2001.18": {"snippet": "A Stemming Algorithmm for the Portuguese Language", "weights": [[2, 10, 1.0]]}, "2003.sigirconf_conference-2003.96": {"snippet": "Stemming in the language modeling framework", "weights": [[0, 8, 1.0]]}, "Y15-2019": {"snippet": "...ion (stemming). Stemmers have improved many NLP tasks without explicit knowledge about its stemming accuracy. In this paper, a study is conducted to evaluate various Arabic stemmers. The study is done...", "weights": [[8, 16, 1.0], [94, 102, 1.0], [169, 175, 1.0]]}, "2008.ipm_journal-ir0volumeA44A1.18": {"snippet": "...eral stemming strategies for this language, and also demonstrates that a light stemming approach could be quite effective. Based on searches done on the CLEF test collection, we find that a more aggre...", "weights": [[8, 16, 1.0], [82, 90, 1.0]]}, "2002.cikm_conference-2002.43": {"snippet": "...ight stemming approach\n\n\n ABSTRACTThe inflectional structure of a word impacts the retrieval accuracy of information retrieval systems of Latin-based languages. We present two stemming algorithms for ...", "weights": [[8, 16, 1.0], [153, 162, 1.0], [179, 187, 1.0]]}, "2006.ipm_journal-ir0volumeA42A4.8": {"snippet": "...lish/Arabic bilingual dictionary from parallel texts that exist in the Internet archive after using an Arabic light stemmer as a preprocessing step. Before using the Arabic light stemmer, the total sy...", "weights": [[8, 14, 1.0], [106, 112, 1.0], [169, 175, 1.0]]}, "W16-3912": {"snippet": "...Many stemming algorithms have been developed for Modern Standard Arabic (MSA). Although Arabic tweets and MSA are closely related and share many characteristics, there are substantial differences betw...", "weights": [[8, 16, 1.0], [68, 74, 1.0], [91, 97, 1.0]]}, "2014.airs_conference-2014.28": {"snippet": "... two stemming algorithms (i.e., morphologyand syntax-based lemmatization; and morphology-based Information Science Research Institute (ISRI) stemming on clustering Arabic text dataset. We aim to ident...", "weights": [[8, 16, 1.0], [144, 152, 1.0], [167, 173, 1.0]]}, "1993.jasis_journal-ir0volumeA44A1.0": {"snippet": "Stemming of French Words Based on Grammatical Categories", "weights": [[0, 8, 1.0]]}, "2002.sigirconf_conference-2002.37": {"snippet": "...ving stemming for Arabic information retrieval: light stemming and co-occurrence analysis\n\n\n ABSTRACTArabic, a highly inflected language, requires good stemming for effective information retrieval, ye...", "weights": [[8, 16, 1.0], [21, 27, 1.0], [57, 65, 1.0], [155, 163, 1.0]]}}} -{"dataset": "ir-lab-sose-2024/ir-acl-anthology-20240504-training", "qid": "5", "default_text": "audio based animal recognition", "runs": [{"name": "ir-lab-sose-2024/tira-ir-starter/BM25 (tira-ir-starter-pyterrier)", "P@10": 0.2, "R@100": 0.455, "nDCG@10": 0.359, "Judged@10": 0.3, "relevance": ["1", "1", "U", "0", "U", "U", "U", "U", "U", "U", "U"], "docs": [{"doc_id": "2021.ecir_conference-20212.70", "doc_id_to_offset": {"start": 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In particular, we introduce a moving object data mining system, MoveMine,...", "weights": [[8, 14, 1.0], [104, 110, 1.0]]}, "2019.clef_conference-2019w.125": {"snippet": "...Bird Recognition in Soundscapes\n\n\n The BirdCLEF challenge-as part of the 2019 LifeCLEF Lab [7]-offers a large-scale proving ground for system-oriented evaluation of bird species identification based o...", "weights": [[8, 19, 1.0], [196, 201, 1.0]]}, "2018.tois_journal-ir0volumeA36A3.7": {"snippet": "...eech recognition technologies, studies of mobile search using voice started to emerge. Many of the studies focused on voice recognition challenges. defined voice search as \"the technology underlying ...", "weights": [[8, 19, 1.0], [127, 138, 1.0]]}, "N19-2025": {"snippet": "Audio De-identification - a New Entity Recognition Task\n\n\n Named Entity Recognition (NER) has been mostly studied in the context of written text. Specifically, NER is an important step in de-identific...", "weights": [[0, 5, 1.0], [39, 50, 1.0], [72, 83, 1.0]]}, "2020.lrec-1.441": {"snippet": "...eech Recognition\n\n\n We present a corpus of sentence-aligned triples of German audio, German text, and English translation, based on German audio books. The speech translation data consist of 110 hours...", "weights": [[8, 19, 1.0], [81, 86, 1.0], [126, 131, 1.0], [142, 147, 1.0]]}, "2014.clef_conference-2014w.63": {"snippet": "...ance-based classification scheme based on the dense indexing of MFCC features and the pruning of the non-discriminant ones. To make such strategy scalable to the 30M of MFCC features extracted from th...", "weights": [[8, 13, 1.0], [36, 41, 1.0]]}, "2004.wwwconf_conference-2004at.73": {"snippet": "... web-based graphics is that a diagram should be able to directly support interaction.Unfortunately, the current vector-based graphics standard SVG 1.1 does not explicitly support these. Instead, the d...", "weights": [[8, 13, 1.0], [122, 127, 1.0]]}, "W14-5131": {"snippet": "... new audio-visual emotion (AVE) database is developed. The database consists of audio, video and audio-visual clips sourced from TV broadcast like movies and soapoperas in English language. The data c...", "weights": [[8, 13, 1.0], [83, 88, 1.0], [100, 105, 1.0]]}, "2004.jasis_journal-ir0volumeA55A12.7": {"snippet": "Recognition and analysis of audio for copyright protection: The RAA project", "weights": [[0, 11, 1.0], [28, 33, 1.0]]}, "2018.ismir_conference-2018.73": {"snippet": "...n of Audio Features for Recognition of Western and Ethnic Instruments in Polyphonic Mixtures", "weights": [[8, 13, 1.0], [27, 38, 1.0]]}, "2017.clef_conference-2017w.88": {"snippet": "...rine Animal Recognition task, we examined different neural networks to make classification based on weakly-labelled images. Our methods achieve good results in both task1 and task3.", "weights": [[8, 14, 1.0], [15, 26, 1.0], [94, 99, 1.0]]}, "2018.wwwjournals_journal-ir0volumeA21A6.19": {"snippet": "...eter-based activity recognition\n\n\n Abstract Accelerometer-based activity recognition (AAR) attracted a lot of attentions due to the wide spread of smartphones with energy-efficiency. However, since ac...", "weights": [[8, 13, 1.0], [23, 34, 1.0], [61, 66, 1.0], [76, 87, 1.0]]}, "2019.clef_conference-2019w.102": {"snippet": "...n-v3 Based Method of LifeCLEF 2019 Bird Recognition\n\n\n In this paper, we present a method of bird recognition based on Inception-v3. The goal of the LifeCLEF2019 Bird Recognition is to detect and clas...", "weights": [[8, 13, 1.0], [43, 54, 1.0], [101, 112, 1.0], [113, 118, 1.0], [170, 181, 1.0]]}, "L08-1469": {"snippet": "...ideo-Based Automatic Sign Language Recognition", "weights": [[8, 13, 1.0], [38, 49, 1.0]]}, "1999.sigirconf_conference-99.6": {"snippet": "...eech recognition (ASR) technology, it is now possible to automatically transcribe speech with reasonable accuracy 17]. Once the contents of a speech database or the audio portions of a multimedia data...", "weights": [[8, 19, 1.0], [168, 173, 1.0]]}, "2018.chiir_conference-2018.58": {"snippet": "...eech recognition (ASR) is reported to be on a par with human performance [25; 26], while high quality synthetic voices generated with deep neural networks (WaveNet Model) can sound almost indistinguis...", "weights": [[8, 19, 1.0]]}, "W17-2003": {"snippet": "...gate animal recognition models learned from wildlife video documentaries by using the weak supervision of the textual subtitles. This is a challenging setting, since i) the animals occur in their natu...", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "2004.ismir_conference-2004.22": {"snippet": "...core-based Gestures In Audio Recordings", "weights": [[8, 13, 1.0], [26, 31, 1.0]]}, "2017.ismir_conference-2017.45": {"snippet": "...king-Based Emotion Recognition for Experimental Music", "weights": [[8, 13, 1.0], [22, 33, 1.0]]}, "2001.sigirconf_conference-2001.57": {"snippet": "...tent-Based Segmentation of Speech Transcripts\n\n\n ABSTRACTAn algorithm for the segmentation of an audio/video source into topically cohesive segments based on automatic speech recognition (ASR) transcr...", "weights": [[8, 13, 1.0], [100, 105, 1.0], [152, 157, 1.0], [178, 189, 1.0]]}, "2014.airs_conference-2014.24": {"snippet": "...tity recognition from the newspaper articles. Thus, this study designs and develops a crime named entity recognition based on machine learning approaches that extract nationalities, weapons, and crime...", "weights": [[8, 19, 1.0], [108, 119, 1.0], [120, 125, 1.0]]}, "2015.clef_conference-2015.39": {"snippet": "...s in Audio Recordings", "weights": [[8, 13, 1.0]]}, "2016.clef_conference-2016w.53": {"snippet": "...s in Audio Recordings using Deep Convolutional Neural Networks\n\n\n This paper summarizes a method for purely audio-based bird species recognition through the application of convolutional neural network...", "weights": [[8, 13, 1.0], [111, 116, 1.0], [117, 122, 1.0], [136, 147, 1.0]]}, "2009.ipm_journal-ir0volumeA45A3.0": {"snippet": "...tion recognition from speech\n\n\n a b s t r a c tEmotional expression and understanding are normal instincts of human beings, but automatical emotion recognition from speech without referring any langua...", "weights": [[8, 19, 1.0], [151, 162, 1.0]]}, "2019.wwwjournals_journal-ir0volumeA22A2.21": {"snippet": "...tion recognition with web-scale video data\n\n\n AbstractAction recognition in surveillance video makes our life safer by detecting the criminal events or predicting violent emergencies. However, efficie...", "weights": [[8, 19, 1.0], [64, 75, 1.0]]}, "2018.clef_conference-2018w.16": {"snippet": "Audio-based Bird Species Identification with Deep Convolutional Neural Networks\n\n\n This paper presents deep learning techniques for audio-based bird identification at very large scale. Deep Convolutio...", "weights": [[0, 5, 1.0], [6, 11, 1.0], [132, 137, 1.0], [138, 143, 1.0]]}, "1999.sigirconf_conference-99.68": {"snippet": "...ideo/audio in order to facilitate rapid retrieval, assimilation and browsing. CueVideo employs a combination of automatic indexing and analysis techniques in the domain of video, audio, speech recogni...", "weights": [[8, 13, 1.0], [182, 187, 1.0], [196, 207, 1.0]]}, "W15-5105": {"snippet": "Synthesizing and Evaluating Animations of {A}merican {S}ign {L}anguage Verbs Modeled from Motion-Capture Data\n\n\n Animations of American Sign Language (ASL) can make information accessible for many sig...", "weights": []}, "2020.signlang-1.6": {"snippet": "{PE}2{LGP} Animator: A Tool To Animate A {P}ortuguese {S}ign {L}anguage Avatar\n\n\n Software for the production of sign languages is much less common than for spoken languages. Such software usually rel...", "weights": []}, "2005.ismir_conference-2005.108": {"snippet": "...enre Recognition Systems in Audio Signals", "weights": [[8, 19, 1.0], [31, 36, 1.0]]}, "2019.clef_conference-2019w.34": {"snippet": "... for audio-based bird identification in soundscapes. Deep Convolutional Neural Networks are trained to classify 659 species. Different data augmentation techniques are applied to prevent overfitting a...", "weights": [[8, 13, 1.0], [14, 19, 1.0]]}, "O11-2002": {"snippet": "...cies Recognition Based on Speech Processing Techniques) [In {C}hinese]", "weights": [[8, 19, 1.0], [20, 25, 1.0]]}, "O14-2004": {"snippet": "...sing audio data collected from commercial CDs and websites show that the timbre-based, pitch-based, and the combination thereof systems achieve 71.1%, 72.1%, and 75.0% accuracy of bird sound identific", "weights": [[8, 13, 1.0], [83, 88, 1.0], [96, 101, 1.0]]}, "2013.tois_journal-ir0volumeA31A2.1": {"snippet": "...tity recognition\n\n\n Named Entity Recognition (NER) is a fundamental task in information extraction from unstructured text. Most previous machine-learning-based NER systems are domain-specific, which i...", "weights": [[8, 19, 1.0], [36, 47, 1.0], [157, 162, 1.0]]}, "2014.clef_conference-2014w.69": {"snippet": "... raw audio without segmentation and without using any auxiliary metadata. It successfully classifies among 501 bird categories, and was by far the highest scoring audio-only bird recognition algorithm...", "weights": [[8, 13, 1.0], [166, 171, 1.0], [182, 193, 1.0]]}, "2007.ismir_conference-2007.59": {"snippet": "...hord Recognition in Audio", "weights": [[8, 19, 1.0], [23, 28, 1.0]]}, "2020.lrec-1.555": {"snippet": "...ties recognition which could be integrated into ISTEX resources. This led to testing two detection tools for animal species names in a corpus of 100 documents in zoology. This makes it possible to pro...", "weights": [[8, 19, 1.0], [112, 118, 1.0]]}, "2018.clef_conference-2018w.99": {"snippet": "...l or audio observations of living organism. A picture actually contains only a partial information that is usually not sufficient to determine the right species with certainty. Quantifying this uncert...", "weights": [[8, 13, 1.0]]}, "2020.cikm_conference-2020.223": {"snippet": "...aker recognition models show that adding certain background sounds to clean vocal audio leads to a substantial deterioration of recognition performance. In several noisy settings, our findings reveal ...", "weights": [[8, 19, 1.0], [85, 90, 1.0], [131, 142, 1.0]]}, "2011.sigirconf_conference-2011.210": {"snippet": "...ning audio-visual and textual cues to improve written word recognition and vocabulary skills. JuSe provides an interactive learning environment that allows parents to introduce new words and concepts ...", "weights": [[8, 13, 1.0], [62, 73, 1.0]]}, "2000.riao_conference-2000.9": {"snippet": "...eech Recognition based Audio Indexing System for the Web", "weights": [[8, 19, 1.0], [20, 25, 1.0], [26, 31, 1.0]]}, "2016.wwwjournals_journal-ir0volumeA19A2.6": {"snippet": "...tion recognition\n\n\n AbstractAs an important task in computer vision, the interaction recognition has attracted extensive attention due to its widely potential applications. The existing methods mainly...", "weights": [[8, 19, 1.0], [88, 99, 1.0]]}, "2021.findings-acl.455": {"snippet": "...e an animal looks like another animal, the more likely it is to survive. Contextual: It's a way of thinking about how organisms adapt to change. The newly found cat is probably part of a separate bran...", "weights": [[8, 14, 1.0], [34, 40, 1.0]]}, "H89-1025": {"snippet": "...tics Based Speech Recognition\n\n\n The objective of this project is to develop a robust and high-performance speech recognitiotl system using a segment-based approach to phonetic recognition. The recogn...", "weights": [[8, 13, 1.0], [21, 32, 1.0], [153, 158, 1.0], [180, 191, 1.0], [197, 208, 1.0]]}, "2018.mir_conference-2018.12": {"snippet": "... for Audio Event Recognition", "weights": [[8, 13, 1.0], [20, 31, 1.0]]}, "2017.clef_conference-2017w.118": {"snippet": "...s in Audio Files Using Transfer Learning\n\n\n In this paper, a method to identify bird species in audio recordings is presented. For this purpose, a pre-trained Inception-v3 convolutional neural network...", "weights": [[8, 13, 1.0], [99, 104, 1.0]]}, "2016.clef_conference-2016w.55": {"snippet": "Audio Based Bird Species Identification using Deep Learning Techniques\n\n\n In this paper we present a new audio classification method for bird species identification. Whereas most approaches apply near...", "weights": [[0, 5, 1.0], [6, 11, 1.0], [105, 110, 1.0]]}, "2019.ismir_conference-2019.4": {"snippet": "...hord Recognition from Audio", "weights": [[8, 19, 1.0], [25, 30, 1.0]]}, "2001.sigirconf_workshop-2001w1.0": {"snippet": "...eech recognition will never be perfect, so analyses applied to the resulting transcripts must be robust in the face of recognition errors. The ability to capture speech and apply speech recognition on...", "weights": [[8, 19, 1.0], [122, 133, 1.0], [189, 200, 1.0]]}, "2014.ismir_conference-2014.48": {"snippet": "...ical Audio Recordings", "weights": [[8, 13, 1.0]]}, "2014.mir_workshop-2014emr.3": {"snippet": "...oint-based medialness for animal and plant identification", "weights": [[8, 13, 1.0], [29, 35, 1.0]]}, "2013.ismir_conference-2013.55": {"snippet": "Audio Chord Recognition with Recurrent Neural Networks", "weights": [[0, 5, 1.0], [12, 23, 1.0]]}, "S19-1032": {"snippet": "Generating Animations from Screenplays\n\n\n Automatically generating animation from natural language text finds application in a number of areas e.g. movie script writing, instructional videos, and publ...", "weights": []}, "W17-7522": {"snippet": "Tutorial for Deaf {--} Teaching {P}unjabi Alphabet using Synthetic Animations\n\n\n Developing an automatic tool for educating students has become essential in today's world of computerization. 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Indeed, it is even difficult, if not impossible, to predict a model's empiri...", "weights": [[8, 17, 1.0], [38, 47, 1.0], [48, 54, 1.0], [104, 113, 1.0]]}, "2017.fire_conference-2017w.24": {"snippet": "...tion Retrieval From Legal Documents\n\n\n This paper details the approach of implementing the Catchphrase Extraction and Precedence Retrieval tasks to be presented at Information Retrieval from Legal Doc...", "weights": [[8, 17, 1.0], [132, 141, 1.0], [179, 188, 1.0]]}, "2011.sigirconf_conference-2011.198": {"snippet": "A comparison of time-aware ranking methods\n\n\n ABSTRACTWhen searching a temporal document collection, e.g., news archives or blogs, the time dimension must be explicitly incorporated into a retrieval m...", "weights": [[2, 12, 1.0], [189, 198, 1.0]]}, "2012.cikm_conference-2012.15": {"snippet": "...tion retrieval evaluation most often involves manually assessing the relevance of particular query-document pairs. In cases where this is difficult (such as personalized search), interleaved compariso...", "weights": [[8, 17, 1.0], [194, 204, 1.0]]}, "2019.ecir_conference-20192.47": {"snippet": "...ased Models for Social Media Retrieval\n\n\n Abstract. The huge amount of information that is available in social media today has resulted in a pressing demand for information retrieval models to support...", "weights": [[8, 14, 1.0], [32, 41, 1.0], [176, 185, 1.0], [186, 192, 1.0]]}, "1992.sigirjournals_journal-ir0volumeA26A2.3": {"snippet": "... for Retrieval of Data on Multimedia Documents\n\n\n AbstractTraditionally, two paradigms are discerned with respect to data retrieval :1. A query mechanism enables the user to express conditions that ch...", "weights": [[8, 17, 1.0], [125, 134, 1.0]]}, "2005.airs_conference-2005.5": {"snippet": "...mage Retrieval Method Using Color Distribution and Common Bitmap\n\n\n Abstract. Image retrieval has emerged as an important problem in multimedia database management. 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Our retrieval experiments focus on i) i...", "weights": [[8, 17, 1.0], [60, 69, 1.0], [168, 177, 1.0]]}, "1980.sigirconf_conference-80.3": {"snippet": "A Comparison of Two Weighting Schemes for Boolean Retrieval", "weights": [[2, 12, 1.0], [50, 59, 1.0]]}, "2006.sigirconf_conference-2006.16": {"snippet": "...tion retrieval\n\n\n ABSTRACTA common limitation of many retrieval models, including the recently proposed axiomatic approaches, is that retrieval scores are solely based on exact (i.e., syntactic) match...", "weights": [[8, 17, 1.0], [57, 66, 1.0], [67, 73, 1.0], [137, 146, 1.0]]}, "2020.clssts-1.6": {"snippet": "A Comparison of Unsupervised Methods for Ad hoc Cross-Lingual Document Retrieval\n\n\n We address the problem of linking related documents across languages in a multilingual collection. 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Firs...", "weights": [[8, 14, 1.0], [105, 114, 1.0], [172, 181, 1.0]]}, "2017.ictir_conference-2017.45": {"snippet": "...rm a comparison of widely-used click models in terms of the quality of click simulation and analyze this quality for queries with di erent frequencies. We nd that click models fail to accurately simul...", "weights": [[8, 18, 1.0], [40, 46, 1.0], [172, 178, 1.0]]}, "P01-1004": {"snippet": "...uity models (in the form of N-grams). Over two distinct datasets, we find that indexing according to simple character bigrams produces a retrieval accuracy superior to any of the tested word Ngram mod...", "weights": [[8, 14, 1.0], [140, 149, 1.0], [200, 206, 1.0]]}, "2013.tois_journal-ir0volumeA31A4.0": {"snippet": "...aved comparison methods, which compare rankers using click data obtained when interleaving their rankings.In this article, we propose a framework for analyzing interleaved comparison methods. An inter...", "weights": [[8, 18, 1.0], [175, 185, 1.0]]}, "1999.sigirconf_conference-99.30": {"snippet": "...ased retrieval system performs well in comparison to conventional retrieval techniques. This prototype system only begins to tap the full potential of translation-based retrieval.", "weights": [[8, 17, 1.0], [42, 52, 1.0], [69, 78, 1.0], [172, 181, 1.0]]}, "2014.cikm_conference-2014.87": {"snippet": "...port comparison shopping. One key user activity is to compare two (or more) products based on a specific aspect. However, making a comparison across two different reviews, written by different authors...", "weights": [[8, 18, 1.0], [134, 144, 1.0], [156, 165, 1.0], [186, 195, 1.0]]}, "2003.clef_workshop-2003.37": {"snippet": "...or a comparison of different domain-specific retrieval systems and techniques. We felt that it was necessary to acquire knowledge on the pros and cons of different retrieval systems through practical ...", "weights": [[8, 18, 1.0], [22, 31, 1.0], [48, 57, 1.0], [157, 166, 1.0], [167, 176, 1.0]]}, "2009.cikm_conference-2009.301": {"snippet": "...tive comparison of retrieval systems based on clickthroughs\n\n\n ABSTRACTThe Cranfield evaluation method has some disadvantages, including its high cost in labor and inadequacy for evaluating interactiv...", "weights": [[8, 18, 1.0], [22, 31, 1.0]]}, "2014.wsdm_conference-2014.18": {"snippet": "...aive comparison can be biased because exposed and unexposed populations typically have different features. One solid methodology for a fair comparison is to apply inverse propensity weighting with dou...", "weights": [[8, 18, 1.0], [90, 99, 1.0], [143, 153, 1.0]]}, "1981.jasis_journal-ir0volumeA32A4.5": {"snippet": "A comparison of two systems of weighted boolean retrieval", "weights": [[2, 12, 1.0], [48, 57, 1.0]]}, "2008.airs_conference-2008.5": {"snippet": "...re's retrieval performance, on different feature spaces? In this paper, we summarize fourteen core dissimilarity measures and classify them into three categories. A systematic performance comparison i...", "weights": [[8, 17, 1.0], [34, 43, 1.0], [191, 201, 1.0]]}, "2015.ecir_conference-2015.22": {"snippet": "... and Retrieval Bias: A Comparison of Inequality Measures", "weights": [[8, 17, 1.0], [26, 36, 1.0]]}, "2013.ictir_conference-2013.3": {"snippet": "...imal retrieval model, enables analytical comparison of different retrieval models without necessarily requiring empirical evaluation, and has led to the development of multiple more effective retrieva...", "weights": [[8, 17, 1.0], [44, 54, 1.0], [58, 67, 1.0], [68, 77, 1.0], [78, 84, 1.0], [195, 204, 1.0]]}, "1994.trec_conference-1994.7": {"snippet": "Comparison of Fragmentation Schemes for Document Retrieval", "weights": [[0, 10, 1.0], [49, 58, 1.0]]}, "2017.sigirconf_conference-2017.203": {"snippet": "...ined models. In addition, the implementations provided for even the most traditional IR evaluation metrics di er from library to library, thus making the objective evaluation and comparison between tr...", "weights": [[8, 14, 1.0], [182, 192, 1.0]]}, "2020.ictir_conference-2020.24": {"snippet": "Taking the Counterfactual Online: Efficient and Unbiased Online Evaluation for Ranking\n\n\n Counterfactual evaluation can estimate Click-Through-Rate (CTR) differences between ranking systems based on h...", "weights": []}, "2011.tois_journal-ir0volumeA29A2.0": {"snippet": "...tion Retrieval Models\n\n\n Developing effective retrieval models is a long-standing central challenge in information retrieval research. In order to develop more effective models, it is necessary to und...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [49, 58, 1.0], [59, 65, 1.0], [118, 127, 1.0], [173, 179, 1.0]]}, "P06-1076": {"snippet": "A Comparison of Document, Sentence, and Term Event Spaces\n\n\n The trend in information retrieval systems is from document to sub-document retrieval, such as sentences in a summarization system and word...", "weights": [[2, 12, 1.0], [86, 95, 1.0], [137, 146, 1.0]]}, "2017.ictir_conference-2017.17": {"snippet": "...uage models in the language modeling framework to ad hoc retrieval. Since KL is rank equivalent to a speci c weighted geometric mean, we examine alternative weighted means for language-model compariso...", "weights": [[8, 14, 1.0], [60, 69, 1.0], [194, 204, 1.0]]}, "2019.ecir_conference-20191.25": {"snippet": "...king Models in an Online Setting\n\n\n Abstract. Online Learning to Rank (OLTR) methods optimize ranking models by directly interacting with users, which allows them to be very efficient and responsive. ...", "weights": [[8, 14, 1.0], [105, 111, 1.0]]}, "2012.sigirjournals_journal-ir0volumeA46A2.13": {"snippet": "...tion retrieval: bridging the theory-effectiveness gap\n\n\n AbstractImproving the effectiveness of general retrieval models has been a long-standing difficult challenge in information retrieval research,...", "weights": [[8, 17, 1.0], [107, 116, 1.0], [117, 123, 1.0], [184, 193, 1.0]]}, "2011.wwwconf_conference-2011.28": {"snippet": "...over different topics of interests that are coherent in geographical regions? (2) how to compare several topics across different geographical locations? To answer these questions, this paper proposes ...", "weights": [[8, 17, 1.0], [122, 131, 1.0]]}, "2016.sigirconf_conference-2016.94": {"snippet": "...tion Retrieval via Topic Modelling\n\n\n ABSTRACTTraditional information retrieval (IR) models, in which a document is normally represented as a bag of words and their frequencies, capture the term-level...", "weights": [[8, 17, 1.0], [73, 82, 1.0], [88, 94, 1.0]]}, "2014.cikm_conference-2014.12": {"snippet": "A Comparison of Retrieval Models using Term Dependencies\n\n\n ABSTRACTA number of retrieval models incorporating term dependencies have recently been introduced. Most of these modify existing \"bag-ofwor...", "weights": [[2, 12, 1.0], [16, 25, 1.0], [26, 32, 1.0], [80, 89, 1.0], [90, 96, 1.0]]}, "1992.jasis_journal-ir0volumeA43A5.2": {"snippet": "Retrieval Testing by the Comparison of Statistically Independent Retrieval Methods", "weights": [[0, 9, 1.0], [25, 35, 1.0], [65, 74, 1.0]]}, "2016.ictir_conference-2016.35": {"snippet": "...s of retrieval accuracy. This paper attempts to fill in this void by reporting the results of an empirical comparison of document expansion methods using translation models estimated based on word co-...", "weights": [[8, 17, 1.0], [110, 120, 1.0], [169, 175, 1.0]]}, "C10-2174": {"snippet": "...ment Retrieval\n\n\n The field of information retrieval still strives to develop models which allow semantic information to be integrated in the ranking process to improve performance in comparison to st...", "weights": [[8, 17, 1.0], [46, 55, 1.0], [81, 87, 1.0], [187, 197, 1.0]]}, "2010.sigirconf_conference-2010.208": {"snippet": "...ring models, making differences hard to appreciate. Seminal efforts on the detection of text reuse have fostered the composition of standard resources for the accurate evaluation and comparison of me...", "weights": [[8, 14, 1.0], [187, 197, 1.0]]}, "2007.sigirconf_conference-2007.152": {"snippet": "A comparison of sentence retrieval techniques", "weights": [[2, 12, 1.0], [25, 34, 1.0]]}, "2020.sigirconf_conference-2020.110": {"snippet": "...ious retrieval criteria, like different ranking models and values of k. In this paper, we conduct the first extensive comparison between ten effective strategies, many of which were never compared bef...", "weights": [[8, 17, 1.0], [33, 42, 1.0], [51, 57, 1.0], [121, 131, 1.0]]}, "P99-1027": {"snippet": "...tion models for both translation directions using the same training data. We investigate information retrieval between English and French, incorporating both translations directions into both document...", "weights": [[8, 14, 1.0], [104, 113, 1.0]]}, "2008.sigirconf_conference-2008.55": {"snippet": "...stic models for passage based retrieval\n\n\n ABSTRACTThe approach of using passage-level evidence for document retrieval has shown mixed results when it is applied to a variety of test beds with differe...", "weights": [[8, 14, 1.0], [33, 42, 1.0], [112, 121, 1.0], [196, 205, 1.0]]}, "2021.eacl-main.293": {"snippet": "...ased models in real-world ad-hoc retrieval applications by comparison to a \"greener and more sustainable\" alternative, comprising only 620 trainable parameters. We present an analysis of their efficac...", "weights": [[8, 14, 1.0], [36, 45, 1.0], [62, 72, 1.0]]}, "2007.ecir_conference-2007.14": {"snippet": "...ance Comparison of Clustered and Replicated Information Retrieval Systems", "weights": [[8, 18, 1.0], [59, 68, 1.0]]}, "2011.ntcir_workshop-2011.18": {"snippet": "...uage models (LM) representing different semantic roles and scopes of models for documents and a rank aggregation method. The rationale is based on observation that sentence-based language models using...", "weights": [[8, 14, 1.0], [33, 42, 1.0], [72, 78, 1.0], [191, 197, 1.0]]}, "2004.ipm_journal-ir0volumeA40A5.2": {"snippet": "...rove retrieval effectiveness. This paper examines the impact the various types of link-based evidence and their combination with content-based evidence have on the retrieval effectiveness for the topi...", "weights": [[8, 17, 1.0], [167, 176, 1.0]]}, "2020.wsdm_conference-2020.105": {"snippet": "... Hoc Retrieval\n\n\n We present Capreolus, a toolkit designed to facilitate end-to-end ad hoc retrieval experiments with neural networks by providing implementations of prominent neural ranking models wi...", "weights": [[8, 17, 1.0], [94, 103, 1.0], [194, 200, 1.0]]}, "2011.sigirconf_conference-2011.145": {"snippet": "...duct comparison using comparative relations\n\n\n ABSTRACTThis paper proposes a novel Product Comparison approach. The comparative relations between products are first mined from both user reviews on mul...", "weights": [[8, 18, 1.0], [94, 104, 1.0]]}, "2006.airs_conference-2006.60": {"snippet": "...ical Comparison of Translation Disambiguation Techniques for Chinese-English Cross-Language Information Retrieval", "weights": [[8, 18, 1.0], [107, 116, 1.0]]}, "2003.sigirconf_conference-2003.35": {"snippet": "...ting retrieval models in the context of patent retrieval. The relative superiority among existing retrieval models did not significantly differ depending on the document genre, that is, patents and ne...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [50, 59, 1.0], [101, 110, 1.0], [111, 117, 1.0]]}, "2014.sigirconf_conference-2014.219": {"snippet": "...tion retrieval models; retrieval constraints; optimization\nOVERVIEWOne of the most important research problems in Information Retrieval (IR) is to develop optimal general IR models. Since IR models de...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [26, 35, 1.0], [129, 138, 1.0], [177, 183, 1.0], [194, 200, 1.0]]}, "2018.ictir_conference-2018.1": {"snippet": "...ical models (e.g., click models) with user behavior hypothesis to extract and train ranking systems with unbiased relevance signals. Recently, a novel counterfactual learning framework that estimates ...", "weights": [[8, 14, 1.0], [28, 34, 1.0]]}, "2011.sigirconf_conference-2011.45": {"snippet": "...wise comparison between two users as a two-player competition with one winner and one loser. Two competition models are proposed to estimate user expertise from pairwise comparisons. Using the NTCIR-8...", "weights": [[8, 18, 1.0], [112, 118, 1.0]]}, "W19-8504": {"snippet": "...n of comparison on a cognitive and functional basis, 1 investigating how two or more entities could be compared in a language, what patterns are used in these various ways of comparison in Latin, and ...", "weights": [[8, 18, 1.0], [178, 188, 1.0]]}, "2009.cikm_conference-2009.134": {"snippet": "...opic retrieval\n\n\n ABSTRACTTraditional models of information retrieval assume documents are independently relevant. But when the goal is retrieving diverse or novel information about a topic, retrieval...", "weights": [[8, 17, 1.0], [41, 47, 1.0], [63, 72, 1.0], [194, 203, 1.0]]}, "O12-2004": {"snippet": "...sive comparison of various topic modeling approaches, including the so-called document topic models (DTM) and word topic models (WTM), for Chinese spoken document retrieval (SDR). Moreover, different ...", "weights": [[8, 18, 1.0], [96, 102, 1.0], [124, 130, 1.0], [166, 175, 1.0], [193, 202, 1.0]]}, "2018.wsdm_conference-2018.38": {"snippet": "...-hoc Retrieval\n\n\n ABSTRACTNeural IR models, such as DRMM and PACRR, have achieved strong results by successfully capturing relevance matching signals. We argue that the context of these matching signa...", "weights": [[8, 17, 1.0], [39, 45, 1.0]]}, "2017.ictir_conference-2017.36": {"snippet": "...post-retrieval QPP method predicts the query's performance based on the quality of its retrieved result list .A common approach utilized by many state-of-the art post-retrieval QPP methods is the anal...", "weights": [[8, 17, 1.0], [170, 179, 1.0]]}, "2009.sigirconf_workshop-2009lsdsir.7": {"snippet": "...tion Retrieval Systems: A Posting-Based Approach\n\n\n Static index pruning methods have been proposed to reduce size of the inverted index of information retrieval systems. The goal is to increase effic...", "weights": [[8, 17, 1.0], [155, 164, 1.0]]}, "2006.sigirconf_conference-2006.7": {"snippet": "...stic models. The first of these directly models an expert's knowledge based on the documents that they are associated with, whilst the second locates documents on topic, and then finds the associated ...", "weights": [[8, 14, 1.0], [44, 50, 1.0]]}, "1981.sigirconf_conference-81.10": {"snippet": "...phic Retrieval Systems\n\n\n Abs t ractThe general model and simulation algorithms for bibliographic retrieval systems presented in an earlier paper I are expanded.The new model integrates the physical a...", "weights": [[8, 17, 1.0], [101, 110, 1.0]]}, "2017.cikm_conference-2017.12": {"snippet": "...aved comparison methods generalize interleaved comparison methods to provide a scalable approach for comparing ranking systems based on regular user interactions. Such methods enable the increasingly ...", "weights": [[8, 18, 1.0], [50, 60, 1.0]]}, "2005.ntcir_workshop-2005.76": {"snippet": "...tion Retrieval task at NTCIR-5. We focused on Chinese monolingual retrieval, and aimed to study the effectiveness of language models and different tokenization methods for Chinese. Our main findings a...", "weights": [[8, 17, 1.0], [69, 78, 1.0], [129, 135, 1.0], [140, 149, 1.0]]}, "2009.clef_workshop-20092.13": {"snippet": "Comparison of Several Combinations of Multimodal and Diversity Seeking Methods for Multimedia Retrieval", "weights": [[0, 10, 1.0], [94, 103, 1.0]]}, "N09-2005": {"snippet": "Comparison of Extended Lexicon Models in Search and Rescoring for {SMT}\n\n\n We show how the integration of an extended lexicon model into the decoder can improve translation performance. The model is b...", "weights": [[0, 10, 1.0], [31, 37, 1.0]]}, "2006.clef_workshop-2006.45": {"snippet": "...sage Retrieval for Question Answering\n\n\n Abstract. In this paper we describe the participation of the Universidad Polit\u00e9cnica of Valencia to the 2006 edition, which was focused on the comparison betwe...", "weights": [[8, 17, 1.0], [187, 197, 1.0]]}, "2017.ictir_workshop-2017learner.7": {"snippet": "...mark comparison of learning-to-rank methods using two evaluation measures: the Normalized Winning Number and the Ideal Winning Number. Evaluation results of 87 learning-to-rank methods on 20 datasets ...", "weights": [[8, 18, 1.0]]}, "2009.sigirconf_conference-2009.92": {"snippet": "A comparison of retrieval-based hierarchical clustering approaches to person name disambiguation", "weights": [[2, 12, 1.0], [16, 25, 1.0]]}, "2010.sigirconf_conference-2010.49": {"snippet": "A comparison of general vs personalised affective models for the prediction of topical relevance\n\n\n ABSTRACTInformation retrieval systems face a number of challenges, originating mainly from the seman...", "weights": [[2, 12, 1.0], [50, 56, 1.0], [120, 129, 1.0]]}, "2016.wsdm_conference-2016.26": {"snippet": "... and Comparison Data\n\n\n ABSTRACTWe present a method for learning potentially intransitive preference relations from pairwise comparison and matchup data. Unlike standard preference-learning models tha...", "weights": [[8, 18, 1.0], [128, 138, 1.0], [193, 199, 1.0]]}, "2008.ir_journal-ir0volumeA11A2.0": {"snippet": "...mage retrieval: an experimental comparison", "weights": [[8, 17, 1.0], [35, 45, 1.0]]}, "1992.ipm_journal-ir0volumeA28A4.5": {"snippet": "...ntal Retrieval Comparisons", "weights": [[8, 17, 1.0]]}, "2000.ismir_conference-2000.1": {"snippet": "A Comparison of Language Modeling and Probabilistic Text Information Retrieval Approaches to Monophonic Music Retrieval", "weights": [[2, 12, 1.0], [69, 78, 1.0], [110, 119, 1.0]]}, "2006.cikm_conference-2006.100": {"snippet": "...rect comparison of commercial and academic retrieval system: an initial study", "weights": [[8, 18, 1.0], [46, 55, 1.0]]}, "2013.cikm_conference-2013.189": {"snippet": "...ning comparison queries. We present a novel snowballing algorithm that \"crawls\" comparison queries from search engines via their query autocompletion services. We propose a novel modeling approach tha...", "weights": [[8, 18, 1.0], [83, 93, 1.0]]}, "2012.cikm_conference-2012.210": {"snippet": "...aved comparison outcomes from historical click data\n\n\n ABSTRACTInterleaved comparison methods, which compare rankers using click data, are a promising alternative to traditional information retrieval ...", "weights": [[8, 18, 1.0], [78, 88, 1.0], [193, 202, 1.0]]}, "2020.sigirconf_conference-2020.42": {"snippet": "...tion retrieval (IR) ranking models in production systems continually evolve in response to user feedback, insights from research, and new developments. Rather than investing all engineering resources ...", "weights": [[8, 17, 1.0], [31, 37, 1.0]]}, "D10-1011": {"snippet": "...uage Models for Clinical Information Retrieval\n\n\n The PECO framework is a knowledge representation for formulating clinical questions. Queries are decomposed into four aspects, which are Patient-Probl...", "weights": [[8, 14, 1.0], [40, 49, 1.0]]}, "2008.sigirconf_conference-2008.62": {"snippet": "Retrieval models for question and answer archives\n\n\n ABSTRACTRetrieval in a question and answer archive involves finding good answers for a user's question. In contrast to typical document retrieval, ...", "weights": [[0, 9, 1.0], [10, 16, 1.0], [189, 198, 1.0]]}, "2012.ecir_conference-2012.24": {"snippet": "...tion Retrieval Model\n\n\n Abstract. We present in this paper well-founded cross-language extensions of the recently introduced models in the information-based family for information retrieval, namely th...", "weights": [[8, 17, 1.0], [128, 134, 1.0], [183, 192, 1.0]]}}} -{"dataset": "ir-lab-sose-2024/ir-acl-anthology-20240504-training", "qid": "7", "default_text": "Cache architecture", "runs": [{"name": "ir-lab-sose-2024/tira-ir-starter/BM25 (tira-ir-starter-pyterrier)", "P@10": 0.9, "R@100": 1.0, "nDCG@10": 0.931, "Judged@10": 1.0, "relevance": ["1", "1", "1", "1", "1", "1", "1", "0", "1", "1", "U"], "docs": [{"doc_id": "2006.wwwjournals_journal-ir0volumeA9A3.0", "doc_id_to_offset": {"start": 203170115, "end": 203173570}}, {"doc_id": "2003.wwwconf_conference-2003.42", "doc_id_to_offset": {"start": 195625946, "end": 195627792}}, {"doc_id": "2012.ipm_journal-ir0volumeA48A5.2", "doc_id_to_offset": {"start": 212664513, "end": 212667115}}, {"doc_id": "2006.cikm_conference-2006.79", "doc_id_to_offset": {"start": 176269402, 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{"doc_id": "E91-1044", "doc_id_to_offset": {"start": 137188057, "end": 137189508}}, {"doc_id": "J90-1001", "doc_id_to_offset": {"start": 118589803, "end": 118591265}}, {"doc_id": "C90-3034", "doc_id_to_offset": {"start": 66787477, "end": 66789488}}, {"doc_id": "P92-1005", "doc_id_to_offset": {"start": 117977134, "end": 117978459}}, {"doc_id": "N15-1126", "doc_id_to_offset": {"start": 73654551, "end": 73656400}}], "ranking": [{"rank": 1, "score": 15.105623488329517, "doc_id": "2020.coling-industry.20"}, {"rank": 2, "score": 12.368156784499908, "doc_id": "2012.wsdm_conference-2012.9"}, {"rank": 3, "score": 11.99112542558773, "doc_id": "J87-1005"}, {"rank": 4, "score": 11.920687358824226, "doc_id": "P88-1005"}, {"rank": 5, "score": 11.417453230095916, "doc_id": "P89-1019"}, {"rank": 6, "score": 11.061979905776168, "doc_id": "E91-1044"}, {"rank": 7, "score": 10.984459953815376, "doc_id": "J90-1001"}, {"rank": 8, "score": 10.9558060505405, "doc_id": "C90-3034"}, {"rank": 9, "score": 10.797712988033227, "doc_id": "P92-1005"}, {"rank": 10, "score": 10.627440503058056, "doc_id": "N15-1126"}]}], "docs": {"2013.ntcir_conference-2013.69": {"snippet": "...eing formula as an expression tree. The other index is a table which stores the parent node and the text string for each node in the expression trees. A hundred thousand documents in the NTCIR-10 Math...", "weights": [[8, 15, 1.0]]}, "2019.sigirconf_workshop-2019birndl.12": {"snippet": "...ne a Formula Concept Retrieval challenge with two subtasks: Formula Concept Discovery (FCD) and Formula Concept Recognition (FCR). While the former aims at the definition and exploration of a Formula ...", "weights": [[8, 15, 1.0], [63, 70, 1.0], [99, 106, 1.0], [195, 202, 1.0]]}, "2014.sigirconf_conference-2014.73": {"snippet": "...ient formula index and retrieval from both webpages and PDF documents. Unlike prior studies, which require users to manually input formula markup language as query, the new system enables users to \"co...", "weights": [[8, 15, 1.0], [134, 141, 1.0]]}, "2013.ntcir_conference-2013.84": {"snippet": "...each document. We employ not only a \"static\" document collection consisting of targeted documents but also a \"dynamic\" document collection including web documents related with queries. We expand the q...", "weights": [[8, 16, 1.0], [48, 56, 1.0], [122, 130, 1.0]]}, "2016.sigirconf_conference-2016.203": {"snippet": "...ased formula retrieval in two different modalities: symbolic for text documents and image-based for videos.We believe that we can achieve high quality formula retrieval results using the visual appear...", "weights": [[8, 15, 1.0], [154, 161, 1.0]]}, "J82-1003": {"snippet": "... the formula for the fragment. However, we have sought to reformulate Gazdar's semantic rules so as to obtain more or less 'conventional' logical translations of English sentences, avoiding the interp...", "weights": [[8, 15, 1.0]]}, "E91-1044": {"snippet": "...ault scoping first. Removing logical redundancy is not only interesting per se, but also drastically reduces the processing time. The input and output formats are described through a few access and co...", "weights": [[8, 15, 1.0]]}, "2009.cikm_conference-2009.275": {"snippet": "Smoothing DCG for learning to rank: a novel approach using smoothed hinge functions\n\n\n ABSTRACTDiscounted cumulative gain (DCG) is widely used for evaluating ranking functions. It is therefore natural...", "weights": []}, "2012.cikm_workshop-2012esair.9": {"snippet": "...ical formula search\n\n\n ABSTRACTIn recent years, growing numbers of scientific papers have been published in XML format generating a large published base of MathML-style formulas. Although these formul...", "weights": [[8, 15, 1.0]]}, "2007.riao_conference-2007.16": {"snippet": "An IE and IR Approach to deal with Geographic Information Scope in Textual Documents", "weights": []}, "2017.ecir_workshop-2017smerp.8": {"snippet": "Extracting Humanitarian Information from Tweets\n\n\n In this paper we describe the application of our methods to humanitarian information extraction from tweets and their performance in the scope of the...", "weights": []}, "2019.tois_journal-ir0volumeA37A3.1": {"snippet": "On Annotation Methodologies for Image Search Evaluation\n\n\n Image search engines differ significantly from general web search engines in the way of presenting search results. The difference leads to di...", "weights": []}, "2018.ictir_conference-2018.36": {"snippet": "...tant document features such as term frequency, document frequency, and document length. We investigate in this paper aggregative and statistical document features for document ranking. Shortly speakin...", "weights": [[8, 16, 1.0], [50, 58, 1.0], [74, 82, 1.0], [148, 156, 1.0], [170, 178, 1.0]]}, "2020.clef_conference-2020w.45": {"snippet": "... (2) Formula Retrieval. Four runs were submitted for Task 1 using systems that take advantage of text and formula embeddings. For Task 2, three runs were submitted: one uses only formula embedding, an...", "weights": [[8, 15, 1.0], [109, 116, 1.0], [182, 189, 1.0]]}, "2006.cikm_conference-2006.60": {"snippet": "...of a document, our model also takes into account domain specific knowledge, i.e., how the domain-specific concepts contained in the document affect the document's readability. Three major readability ...", "weights": [[8, 16, 1.0], [135, 143, 1.0], [155, 163, 1.0]]}, "2011.sigirconf_conference-2011.121": {"snippet": "...tion formula:where |D| represents document length, avdl stands for average document length, c(q, D) is the raw TF of q in D, and b and k1 are two parameters. c (q, D) is the normalized TF by document ...", "weights": [[8, 15, 1.0], [37, 45, 1.0], [78, 86, 1.0], [194, 202, 1.0]]}, "J87-1005": {"snippet": "...ored scoping or generated scopings with mechanisms that have often been inexplicit as to the range of scopings they choose among or profligate in the scopings they allow. This paper presents, along wi...", "weights": [[8, 15, 1.0]]}, "P11-2025": {"snippet": "...s on scoping sentences with only two quantified noun phrases (NPs), where the quantifiers are restricted to a predefined list. It also ignores negation, modal/logical operators, and other sentential a...", "weights": [[8, 15, 1.0]]}, "2012.wsdm_conference-2012.9": {"snippet": "...oral scoping. We show that joint inference is effective compared to doing temporal scoping of individual facts independently. We conduct our experiments on large scale open-domain publicly available t...", "weights": [[8, 15, 1.0], [86, 93, 1.0]]}, "2020.coling-industry.20": {"snippet": "...I}t: Scoping Task Relevant Sentences in Documents\n\n\n A prominent problem faced by conversational agents working with large documents (Eg: emailbased assistants) is the frequent presence of information...", "weights": [[8, 15, 1.0]]}, "2004.spire_conference-2004.15": {"snippet": "Indexing Text Documents Based on Topic Identification\n\n\n Abstract. This work provides algorithms and heuristics to index text documents by determining important topics in the documents. To index text ...", "weights": []}, "2003.spire_conference-2003.15": {"snippet": "...king formula which we evaluate using a medical reference collection, the OHSUMED collection. Our results indicate that this combination of evidences might yield considerable gains in retrieval perform...", "weights": [[8, 15, 1.0]]}, "J90-1001": {"snippet": "... and Scoping\n\n\n Certain restrictions on possible scopings of quantified noun phrases in natural language are usually expressed in terms of formal constraints on binding at a level of logical form. Suc...", "weights": [[8, 15, 1.0]]}, "P92-1031": {"snippet": "Connection Relations and Quantifier Scope\n\n\n A formalism will be presented in this paper which makes it possible to realise the idea of assigning only one scope-ambiguous representation to a sentence ...", "weights": []}, "1997.cikm_workshop-97.53": {"snippet": "...nted Document Search System\n\n\n AbstractWith the increasing numbers of documents available from the Internet and other sources, finding anything is becoming increasingly difficult. This paper presents ...", "weights": [[8, 16, 1.0]]}, "2005.ipm_journal-ir0volumeA41A5.6": {"snippet": "Computing, explaining and visualizing shape similarity in content-based image retrieval\n\n\n AbstractAlthough there is a growing need for Content-Based Image Retrieval systems, their use is often hamper...", "weights": []}, "2008.gir_workshop-2008.3": {"snippet": "Geographic scope modeling for web documents", "weights": []}, "J03-1004": {"snippet": "A Machine Learning Approach to Modeling Scope Preferences\n\n\n This article describes a corpus-based investigation of quantifier scope preferences. Following recent work on multimodular grammar framewor...", "weights": []}, "2013.ntcir_conference-2013.75": {"snippet": "...t of formula patterns and a test data corpus. We showcase a simple data analysis program for answering the given queries. We interpret the patterns as regular expressions and assume that matches to th...", "weights": [[8, 15, 1.0]]}, "W14-1612": {"snippet": "...oral Scoping of Relational Facts based on {W}ikipedia Data\n\n\n Most previous work in information extraction from text has focused on named-entity recognition, entity linking, and relation extraction. L...", "weights": [[8, 15, 1.0]]}, "2020.clef_conference-2020w.120": {"snippet": "Named Entity Recognition in Chemical Patents using Ensemble of Contextual Language Models\n\n\n Chemical patent documents describe a broad range of applications holding key reaction and compound informat...", "weights": []}, "2020.lrec-1.269": {"snippet": "...s of formula identifiers and links them to their in-text descriptions, given only the original PDF and the location of the formula of interest. We also present a novel evaluation dataset for this task...", "weights": [[8, 15, 1.0], [126, 133, 1.0]]}, "2020.sdp-1.16": {"snippet": "... and formula analyses, so-called mathematical language processing, is necessary to enable computers to understand and retrieve information from the documents. However, as we will show in this project,...", "weights": [[8, 15, 1.0]]}, "2004.wwwconf_conference-2004at.37": {"snippet": "Enhancing the SCORM metadata model\n\n\n ABSTRACTNowadays, the leading e-learning platforms are converging towards standardization. This paper presents an extension to the SCORM, today's most well acclai...", "weights": []}, "2016.cikm_workshop-2016fm4m.18": {"snippet": "Augmenting Mathematical Formulae for More Effective Querying & Presentation", "weights": []}, "1999.sigirconf_conference-99.12": {"snippet": "...ical Formula for Chinese Text Segmentation Incorporating Contextual Information\n\n\n ABSTRACTA new statistical formula for identifying 2-character words in Chinese text, called the contextual informatio...", "weights": [[8, 15, 1.0], [112, 119, 1.0]]}, "P89-1019": {"snippet": "... and Scoping\n\n\n Certain restrictions on possible scopings of quantified noun phrases in natural language are usually expressed in terms of formal constraints on binding at a level of logical form. Suc...", "weights": [[8, 15, 1.0]]}, "2015.tois_journal-ir0volumeA33A2.3": {"snippet": "...each document is prenormalized by dividing the term frequency by the verbosity of the document. In scope normalization, an existing retrieval model is applied in a straightforward manner to the prenor...", "weights": [[8, 16, 1.0], [89, 97, 1.0]]}, "2009.cikm_workshop-2009swsm.4": {"snippet": "Automobile, car and BMW: horizontal and hierarchical approach in social tagging systems\n\n\n ABSTRACTSocial tagging systems have recently emerged as an effective way for users to annotate and organize l...", "weights": []}, "P88-1005": {"snippet": "...fier Scoping in the {SRI} Core Language Engine\n\n\n An algorithm for generating the possible quantifier scopings for a sentence, in order of preference, is outlined. The scoping assigned to a quantifier...", "weights": [[8, 15, 1.0], [171, 178, 1.0]]}, "2011.tois_journal-ir0volumeA29A2.5": {"snippet": "...ical formula, all possible (or a significant number of) subformulae of formulae that appear in any document and all possible subterms (e.g., \"methyl\") of chemical names (e.g., \"methylethyl ketone\") mu...", "weights": [[8, 15, 1.0], [102, 110, 1.0]]}, "2015.wsdm_conference-2015.62": {"snippet": "... for Document-Level Sentiment Analysis\n\n\n ABSTRACTIn this paper, we propose a representation learning research framework for document-level sentiment analysis. Given a document as the input, document-...", "weights": [[8, 16, 1.0], [128, 136, 1.0], [171, 179, 1.0], [194, 202, 1.0]]}, "C90-3034": {"snippet": "...fier Scoping Algorithm without A Free Variable Constraint\n\n\n Three recent demands on quantifier scoping algorithms have been that they should be explicitly stated, they should be sound and complete wi...", "weights": [[8, 15, 1.0], [99, 106, 1.0]]}, "2021.newsum-1.14": {"snippet": "...each document and intent (e.g., \"weather\"), a single summary suffices for all users. No datasets exist, however, for subjective intents (e.g., \"interesting places\") where different users will provide ...", "weights": [[8, 16, 1.0]]}, "2002.spire_conference-2002.13": {"snippet": "...Rank formula and the classic formula proposed in . As an example of the versatility of our general formula we derive from it a version of PageRank based on a user personalization. Finally, we discuss ...", "weights": [[8, 15, 1.0], [32, 39, 1.0], [102, 109, 1.0]]}, "2007.wwwconf_conference-2007.26": {"snippet": "...ical formula using a search engine today, she gets articles where the exact keyword string expressing the chemical formula is found. Searching for the exact occurrence of keywords during searching res...", "weights": [[8, 15, 1.0], [118, 125, 1.0]]}, "2020.clef_conference-2020w.14": {"snippet": "...t of formula embedding and term-document matrix, in this paper we have introduced the variable size formula embedding approach where the formula is transformed into the variable size vector. In a vect...", "weights": [[8, 15, 1.0], [35, 43, 1.0], [103, 110, 1.0], [140, 147, 1.0]]}, "2011.sigirconf_conference-2011.20": {"snippet": "...with document length based on a trade-off between the Verbosity hypothesis, which assumes the independence of a document's relevance of its length, and the Scope hypothesis, which assumes the opposite...", "weights": [[8, 16, 1.0], [115, 123, 1.0]]}, "Y10-1055": {"snippet": "A Modular Architecture for the Wide-Coverage Translation of Natural Language Texts into Predicate Logic Formulas\n\n\n We present a new method for translating unrestricted natural language texts into pre...", "weights": []}, "C88-2135": {"snippet": "...lity Formula of {J}apanese Texts for Machine Scoring\n\n\n A readability formula is obtained that can be used by computer programs for style checking of Japanese texts and need not syntactic or semantic ...", "weights": [[8, 15, 1.0], [73, 80, 1.0]]}, "2020.lrec-1.132": {"snippet": "...ting scoping configurations, and is presented specifically as a resource for quantifier scope disambiguation systems, but also more generally as an object of linguistic study.", "weights": [[8, 15, 1.0]]}, "2019.wwwconf_conference-2019c.169": {"snippet": "...oral Scoping of Facts", "weights": [[8, 15, 1.0]]}, "2009.tois_journal-ir0volumeA27A3.4": {"snippet": "Bounded coordinate system indexing for real-time video clip search\n\n\n Recently, video clips have become very popular online. The massive influx of video clips has created an urgent need for video sear...", "weights": []}, "2020.ecir_conference-20202.60": {"snippet": "...tive Formula Editing, Reuse, and Lookup\n\n\n There has been growing interest in math-aware search engines that support retrieval using both formulas and keywords. An important unresolved issue is the de...", "weights": [[8, 15, 1.0]]}, "2006.clef_workshop-2006w.114": {"snippet": "...d in document retrieval through a ranking function that combines BM25 text weighting with a similarity function for geographic scopes. We also tested a topic augmentation method, based on the geograph...", "weights": [[8, 16, 1.0]]}, "2020.clef_conference-2020w.8": {"snippet": "...ntic Formula Search in zbMATH Open?", "weights": [[8, 15, 1.0]]}, "A88-1008": {"snippet": "... the scoping protocol which wcre encountered during the design of the scoping program.", "weights": [[8, 15, 1.0], [73, 80, 1.0]]}, "2014.ntcir_conference-2014.19": {"snippet": "Evaluation of Similarity-Measure Factors for Formulae Based on the NTCIR-11 Math Task\n\n\n In this paper we evaluate the similarity-measure factors proposed by Zhang and Youssef based on the NTCIR-11 go...", "weights": []}, "W19-3306": {"snippet": "Generating Discourse Inferences from Unscoped Episodic Logical Formulas\n\n\n Unscoped episodic logical form (ULF) is a semantic representation capturing the predicateargument structure of English within...", "weights": []}, "Y05-1017": {"snippet": "A study on multiple interpretations of frequency adverbs in {J}apanese\n\n\n This paper is concerned with frequency adverbs in Japanese. Many linguists have pointed out that frequency statements and gene...", "weights": []}, "N15-1126": {"snippet": "...gger Scoping Strategy for Biographical Fact Extraction\n\n\n The rapid growth of information sources brings a unique challenge to biographical information extraction: how to find specific facts without h...", "weights": [[8, 15, 1.0]]}, "2007.cikm_workshop-2007cims.8": {"snippet": "...on a document collection. Each method consists of a combination of term scoring scheme, term scoring formula, and term assignment scheme. Our preliminary experiments evaluate these different slot-base...", "weights": [[8, 16, 1.0], [104, 111, 1.0]]}, "W06-1510": {"snippet": "Quantifier Scope in {G}erman: An {MCTAG} Analysis\n\n\n Relative quantifier scope in German depends, in contrast to English, very much on word order. The scope possibilities of a quantifier are determine...", "weights": []}, "2018.sigirconf_conference-2018.210": {"snippet": "...-idf formula for expanded words in search ranking.", "weights": [[8, 15, 1.0]]}, "2013.clef_conference-2013w.64": {"snippet": "German-Language Content in Biomedical Vocabularies\n\n\n There are hundreds of English-language biomedical vocabularies, but only a minor part has been translated into other languages. We performed a sur...", "weights": []}, "P92-1005": {"snippet": "...fier scoping and reference resolution, are often realised computationally by non-monotonic operations involving loss of information and destructive manipulation of semantic representations. The paper ...", "weights": [[8, 15, 1.0]]}, "2004.spire_conference-2004.6": {"snippet": "...hird document. The current technique for concurrently detecting all co-derivatives in a collection is document fingerprinting, which matches documents based on the hash values of selected document sub...", "weights": [[8, 16, 1.0], [105, 113, 1.0], [191, 199, 1.0]]}, "2016.ntcir_conference-2016.87": {"snippet": "...t of formula importance within a document is introduced into the model. In order to make the ranking results more reasonable, our system (WikiMir) applies the learning to rank algorithm (RankBoost) to...", "weights": [[8, 15, 1.0], [36, 44, 1.0]]}, "2009.wsdm_workshop-2009wscd.10": {"snippet": "...rove document descriptions. A pilot experiment demonstrated that document descriptors extracted from the queries that are associated with documents by clicks provide useful semantic information about ...", "weights": [[8, 16, 1.0], [68, 76, 1.0]]}, "J90-3001": {"snippet": "Resolving Quasi Logical Forms\n\n\n The paper describes intermediate and resolved logical form representations of sentences involving referring expressions and a reference resolution process for mapping ...", "weights": []}, "2007.clef_workshop-2007.101": {"snippet": "... and Document Scopes in Geographic IR", "weights": [[8, 16, 1.0]]}, "2020.sigirconf_conference-2020.307": {"snippet": "KLOOS: KL Divergence-based Out-of-Scope Intent Detection in Human-to-Machine Conversations\n\n\n User intent is not restricted in human-to-machine conversations, and sometimes overshoots the scope of a d...", "weights": []}, "L12-1534": {"snippet": "A methodology for the extraction of information about the usage of formulaic expressions in scientific texts", "weights": []}, "2012.tois_journal-ir0volumeA30A2.5": {"snippet": "Authorship Attribution Based on Specific Vocabulary\n\n\n In this article we propose a technique for computing a standardized Z score capable of defining the specific vocabulary found in a text (or part ...", "weights": []}, "P89-1004": {"snippet": "...s of scoping and reference resolution. 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Unfortunately, many experiments have shown that although pseudo-relevance feedb...", "weights": [[8, 16, 1.0], [45, 53, 1.0], [198, 206, 1.0]]}, "2008.sigirconf_conference-2008.32": {"snippet": "...ance feedback\n\n\n ABSTRACTTypical pseudo-relevance feedback methods assume the topretrieved documents are relevant and use these pseudo-relevant documents to expand terms. The initial retrieval set can...", "weights": [[8, 16, 1.0], [53, 61, 1.0]]}, "W03-2004": {"snippet": "...ance Feedback Method based on {T}aylor Expansion of Retrieval Function in {NTCIR}-3 Patent Retrieval Task\n\n\n Pseudo relevance feedback is empirically known as a useful method for enhancing retrieval p...", "weights": [[8, 16, 1.0], [129, 137, 1.0]]}, "2008.trec_conference-2008.16": {"snippet": "...ers' feedback for an initial result list by the system. With different amount of feedback information available, the optimal feedback strategy might be very different. 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Traditional feedback algorithms use a whole feedback document as a unit to extract words f...", "weights": [[8, 16, 1.0], [45, 53, 1.0], [125, 133, 1.0], [157, 165, 1.0]]}, "2012.cikm_workshop-2012dubmmsm.7": {"snippet": "...icit feedback, high proportion of cold-start tweets and short tweet active time. In this paper, we propose a novel retweet recommendation (RTR) framework which leverages the implicit feedback to help ...", "weights": [[8, 16, 1.0], [186, 194, 1.0]]}, "2017.ecir_conference-2017.63": {"snippet": "...tive feedback from the user in a language modeling framework to compute the relevancy score of new documents. In other words, the negative feedback from the user is considered as the representative of...", "weights": [[8, 16, 1.0], [142, 150, 1.0]]}, "2008.sigirconf_conference-2008.57": {"snippet": "...ance feedback. For disjoint terms, a relationship between probability theory and TF-IDF is established through the integral R 1 x dx = log x. This study uncovers components such as divergence from ran...", "weights": [[8, 16, 1.0]]}, "2012.wsdm_conference-2012.59": {"snippet": "Exploring social influence via posterior effect of word-of-mouth recommendations\n\n\n ABSTRACTWord-of-mouth has proven an effective strategy for promoting products through social relations. Particularly...", "weights": []}, "2008.ecir_conference-2008.44": {"snippet": "...ance feedback/query expansion have been reported as two effective means for improving document retrieval in literature. Relevance models, while improving retrieval in most cases, hurts performance on ...", "weights": [[8, 16, 1.0]]}, "2012.irfc_conference-2012.1": {"snippet": "...ance Feedback for Patent Retrieval", "weights": [[8, 16, 1.0]]}, "2008.sigirconf_conference-2008.31": {"snippet": "...ance feedback\n\n\n ABSTRACTRelevance feedback, which traditionally uses the terms in the relevant documents to enrich the user's initial query, is an effective method for improving retrieval performance...", "weights": [[8, 16, 1.0], [38, 46, 1.0]]}, "2005.wwwconf_conference-2005si.112": {"snippet": "...ance feedback\nGeneral TermsAlgorithms, Performance, Experimentation\nKeywordsRelevance Feedback, Web Search, Evaluation\n1.INTRODUCTIONRelevance feedback is a classical information retrieval (IR) techni...", "weights": [[8, 16, 1.0], [89, 97, 1.0], [146, 154, 1.0]]}, "2009.cikm_conference-2009.277": {"snippet": "...ance feedback using semantic clustering in relevance language model\n\n\n ABSTRACTPseudo relevance feedback has demonstrated to be in general an effective technique for improving retrieval effectiveness,...", "weights": [[8, 16, 1.0], [99, 107, 1.0]]}, "2013.sigirconf_conference-2013.57": {"snippet": "...ance feedback\n\n\n ABSTRACTPseudo-relevance feedback is an important strategy to improve search accuracy. It is often implemented as a tworound retrieval process: the first round is to retrieve an initi...", "weights": [[8, 16, 1.0], [45, 53, 1.0]]}, "2006.sigirconf_conference-2006.22": {"snippet": "...ance feedback\n\n\n ABSTRACTPseudo-relevance feedback has proven to be an effective strategy for improving retrieval accuracy in all retrieval models. However the performance of existing pseudo feedback ...", "weights": [[8, 16, 1.0], [45, 53, 1.0], [194, 202, 1.0]]}, "2017.ecir_conference-2017.65": {"snippet": "...ance Feedback", "weights": [[8, 16, 1.0]]}, "2008.ecir_conference-2008.54": {"snippet": "...eudo Feedback Document Selection for Query Expansion", "weights": [[8, 16, 1.0]]}, "2015.clef_conference-2015.8": {"snippet": "...ance feedback helps in such a scenario. Therefore we implemented a number of pseudo relevance feedback techniques based on knowledge bases, where the knowledge base is either Wikipedia or a combinatio...", "weights": [[8, 16, 1.0], [97, 105, 1.0]]}, "2009.trec_conference-2009.5": {"snippet": "...eudo feedback strategy which stops pseudo feedback for precision-oriented queries and only uses it for recall-oriented ones. The proposed PRM has shown clear improvements over the relevance model for ...", "weights": [[8, 16, 1.0], [45, 53, 1.0]]}, "2016.sigirconf_conference-2016.129": {"snippet": "...ance Feedback in Content-Based Image Retrieval\n\n\n ABSTRACTThe increased availability of image capturing devices has enabled collections of digital images to rapidly expand in both size and diversity. ...", "weights": [[8, 16, 1.0]]}, "2010.airs_conference-2010.20": {"snippet": "...ance Feedback Based on mRMR Criteria", "weights": [[8, 16, 1.0]]}, "2007.cikm_conference-2007.91": {"snippet": "...ance feedback. To derive this measure, it is necessary to make the assumption that a user is seeking an ideal (usually non-existent) document and the actual relevant documents in the collection will c...", "weights": [[8, 16, 1.0]]}, "2016.tois_journal-ir0volumeA34A4.3": {"snippet": "...ance Feedback\n\n\n Traditional pseudo relevance feedback (PRF) models choose top k feedback documents for query expansion and treat those documents equally. When k is determined, feedback terms are sele...", "weights": [[8, 16, 1.0], [49, 57, 1.0], [84, 92, 1.0], [180, 188, 1.0]]}, "2007.cikm_conference-2007.31": {"snippet": "...icit feedback\n\n\n ABSTRACTImplicit feedback algorithms utilize interaction between searchers and search systems to learn more about users' needs and interests than expressed in query statements alone. ...", "weights": [[8, 16, 1.0], [37, 45, 1.0]]}, "2003.sigirjournals_journal-ir0volumeA37A2.1": {"snippet": "...icit feedback for inferring user preference: a bibliography\n\n\n Relevance feedback has a history in information retrieval that dates back well over thirty years (c.f [SL96]). Relevance feedback is typi...", "weights": [[8, 16, 1.0], [76, 84, 1.0], [187, 195, 1.0]]}, "2013.ipm_journal-ir0volumeA49A4.3": {"snippet": "...ance feedback\n\n\n a b s t r a c tTypical pseudo-relevance feedback methods assume the top-retrieved documents are relevant and use these pseudo-relevant documents to expand terms. The initial retrieval...", "weights": [[8, 16, 1.0], [60, 68, 1.0]]}, "2009.sigirconf_conference-2009.9": {"snippet": "...ance feedback based on wikipedia", "weights": [[8, 16, 1.0]]}, "2007.clef_workshop-2007w.129": {"snippet": "...ance Feedback to Improve Image Retrieval Results\n\n\n In this paper, we propose a pseudo-relevance feedback method to deal with the photographic retrieval and medical retrieval tasks of ImageCLEF 2007. ...", "weights": [[8, 16, 1.0], [100, 108, 1.0]]}, "2020.ipm_journal-ir0volumeA57A6.55": {"snippet": "...ance feedback framework combining relevance matching and semantic matching for information retrieval", "weights": [[8, 16, 1.0]]}, "2006.sigirconf_conference-2006.110": {"snippet": "...ance feedback results\n\n\n ABSTRACTWe explore interactive methods to further improve the performance of pseudo-relevance feedback. Studies suggest that new methods for tackling difficult queries are re...", "weights": [[8, 16, 1.0], [122, 130, 1.0]]}, "2018.sigirconf_workshop-2018profs.3": {"snippet": "...ance feedback. Our focus in this paper is on one particular aspect of CAL: refreshing, which is a crucial and recurring event in the CAL process. During a refresh, the machine learning model is traine...", "weights": [[8, 16, 1.0]]}, "2001.clef_workshop-2001.12": {"snippet": "...ance Feedback Using a Comparable Corpus", "weights": [[8, 16, 1.0]]}, "2014.ecir_conference-2014.22": {"snippet": "...ance Feedback Techniques for Informal Text", "weights": [[8, 16, 1.0]]}, "2021.ecir_conference-20212.46": {"snippet": "...ance Feedback Using a Graph-Based Transformer\n\n\n Most research on pseudo relevance feedback (PRF) has been done in vector space and probabilistic retrieval models. This paper shows that Transformer-ba...", "weights": [[8, 16, 1.0], [86, 94, 1.0]]}, "2010.trec_conference-2010.29": {"snippet": "...ance Feedback: QUT E-Discovery Lab at the TREC 2010 Relevance Feedback Track\n\n\n User relevance feedback is usually utilized by Web systems to interpret user information needs and retrieve effective re...", "weights": [[8, 16, 1.0], [65, 73, 1.0], [98, 106, 1.0]]}, "2008.sigirconf_conference-2008.56": {"snippet": "...ance feedback algorithm based on the latent mixture modeling of the Dirichlet compound multinomial distribution to further improve retrieval accuracy. Finally, our experiments show that both the basel...", "weights": [[8, 16, 1.0]]}, "2016.cikm_conference-2016.153": {"snippet": "...ance Feedback Based on Matrix Factorization", "weights": [[8, 16, 1.0]]}, "2001.sigirconf_conference-2001.53": {"snippet": "...ance Feedback Using Optimization Tables", "weights": [[8, 16, 1.0]]}, "2018.ictir_conference-2018.35": {"snippet": "...ance Feedback in Fusion-based Retrieval\n\n\n ABSTRACTThe usage of positive relevance feedback in fusion-based retrieval was previously shown to be very useful. Yet, in many retrieval usecases, no actual...", "weights": [[8, 16, 1.0], [86, 94, 1.0]]}, "2011.jasis_journal-ir0volumeA62A4.10": {"snippet": "...ance feedback", "weights": [[8, 16, 1.0]]}, "2011.ictir_conference-2011.30": {"snippet": "...ance Feedback for Ad-hoc Retrieval", "weights": [[8, 16, 1.0]]}, "2008.sigirconf_conference-2008.150": {"snippet": "...ance feedback in language modeling with score regularization\n\n\n ABSTRACTWe demonstrate that regularization can improve feedback in a language modeling framework.", "weights": [[8, 16, 1.0], [122, 130, 1.0]]}, "2010.cikm_conference-2010.111": {"snippet": "...ance feedback algorithms\n\n\n ABSTRACTWe present a flexible new optimization framework for finding effective, reliable pseudo-relevance feedback models that unifies existing complementary approaches in ...", "weights": [[8, 16, 1.0], [137, 145, 1.0]]}, "2018.ipm_journal-ir0volumeA54A2.6": {"snippet": "...on a feedback query model that easily and typically holds in modern pseudo-relevance feedback methods: 1) the verbosity-preserving effect: the query verbosity of a feedback query model is determined b...", "weights": [[8, 16, 1.0], [88, 96, 1.0], [167, 175, 1.0]]}, "2007.sigirconf_conference-2007.41": {"snippet": "...line feedback method as a black box, and the output feedback model as a random variable, we estimate a posterior distribution for the feedback model by resampling a given query's top-retrieved documen...", "weights": [[8, 16, 1.0], [55, 63, 1.0], [137, 145, 1.0]]}, "2004.sigirconf_conference-2004.72": {"snippet": "...eudo feedback documents than an effective existing feedback model. The new model can tolerate a more flexible setting of the number of pseudo feedback documents without the danger of losing much retri", "weights": [[8, 16, 1.0], [54, 62, 1.0], [145, 153, 1.0]]}, "2016.ipm_journal-ir0volumeA52A3.7": {"snippet": "...ance feedback is the basis of a category of automatic query modification techniques. Pseudo-relevance feedback methods assume the initial retrieved set of documents to be relevant. 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We accomplish this by reasoning from a grammar which describes gesture declaratively in terms of its discourse function, semantics and synchrony with speech.", "weights": [[8, 15, 1.0]]}, "2005.wwwconf_conference-2005.25": {"snippet": "Web-assisted annotation, semantic indexing and search of television and radio news\n\n\n ABSTRACTThe Rich News system, that can automatically annotate radio and television news with the aid of resources ...", "weights": []}, "2010.sigirconf_conference-2010.107": {"snippet": "Context aware query classification using dynamic query window and relationship net\n\n\n ABSTRACTThe context of the user queries, preceding a given query, is utilized to improve the effectiveness of quer...", "weights": []}, "2015.tois_journal-ir0volumeA34A1.3": {"snippet": "....e., word embeddings) to address text mining, information retrieval, and natural language processing tasks. Most recent efforts have proposed several efficient methods to learn word embeddings from co...", "weights": [[8, 12, 1.0], [76, 83, 1.0], [180, 184, 1.0]]}, "2020.sigirconf_conference-2020.313": {"snippet": "...ging Word Proximity Networks\n\n\n Question answering (QA) over text passages is a problem of longstanding interest in information retrieval. Recently, the conversational setting has attracted attention,...", "weights": [[8, 12, 1.0]]}, "2017.wwwconf_conference-2017.142": {"snippet": "...cale natural setting, in the form of an online team-based geography game, that allows us to disentangle confidence from competence and thus separate their effects.We find that in task-oriented discuss...", "weights": [[8, 15, 1.0]]}, "2021.gwc-1.3": {"snippet": "...ican Word-Nets in OMW using NLTK.", "weights": [[8, 12, 1.0], [13, 17, 1.0]]}, "E87-1030": {"snippet": "Natural and Simulated Pointing\n\n\n Referent identification in human conversation is performed both by describing the objects in question and by pointing at them. Up till now, only the linguistic compon...", "weights": [[0, 7, 1.0]]}, "2012.cikm_conference-2012.197": {"snippet": "...hich represent the NP, and classifying the NP to a specific broad category such as politics, sports, etc. To classify an NP, we define the feature vector for the NP using not just the words but also t...", "weights": [[8, 17, 1.0]]}, "2018.wwwconf_conference-2018c.101": {"snippet": "...uter Conversations\n\n\n ABSTRACTTo establish an automatic conversation system between human and computer is regarded as one of the most hardcore problems in computer science. It requires interdisciplina...", "weights": [[8, 21, 1.0]]}, "2020.acl-main.184": {"snippet": "...uman conversations naturally evolve around related concepts and scatter to multi-hop concepts. This paper presents a new conversation generation model, ConceptFlow, which leverages commonsense knowled...", "weights": [[8, 21, 1.0]]}, "1998.tois_journal-ir0volumeA16A4.3": {"snippet": "...ntic nets. The relational inconsistency hinders the creation of domain information models. The structural inconsistency leads to unstable documents, especially when a document is composed by computati...", "weights": [[8, 12, 1.0]]}, "2013.cikm_workshop-2013unlp.10": {"snippet": "Mirroring the real world in social media: twitter, geolocation, and sentiment analysis\n\n\n ABSTRACTIn recent years social media has been used to characterize and predict real world events, and in this ...", "weights": []}, "2003.clef_workshop-2003w.3": {"snippet": "...ying Natural Language Processing techniques for single word and multi-word term conflation. Two different conflation approaches have been tested. The first approach is based on the lemmatization of th...", "weights": [[8, 15, 1.0], [58, 62, 1.0], [73, 77, 1.0]]}, "H01-1013": {"snippet": "... key word search do not allow users to describe their intentions precisely. We have developed a conversational interface to online shopping that provides convenient, personalized access to information...", "weights": [[8, 12, 1.0]]}, "2018.sigirconf_conference-2018.26": {"snippet": "...e to represent a user conversation history as a semi-structured user query with facet-value pairs. This query is generated and updated by belief tracker that analyzes natural language utterances of us...", "weights": [[8, 17, 1.0], [170, 177, 1.0]]}, "W10-1302": {"snippet": "...uses Natural Language Generation (NLG) technology to automatically generate conversational utterances from a domain knowledge base modelled from content suggested by a small AAC user group. Findings f...", "weights": [[8, 15, 1.0]]}, "2019.ipm_journal-ir0volumeA56A1.9": {"snippet": "...line conversations\n\n\n A B S T R A C TWith the explosion of multilingual content on Web, particularly in social media platforms, identification of languages present in the text is becoming an important...", "weights": [[8, 21, 1.0]]}, "2021.emnlp-main.174": {"snippet": "...nted conversations, so as to effectively identify user's intention and generate more accurate responses. Experimental results verify the effectiveness of IR-Net, showing that it achieves the stateof-t...", "weights": [[8, 21, 1.0]]}, "P98-1066": {"snippet": "...th a natural language layer to convert English queries for use by the various search engines. The NLP layer incorporates morphological analysis, noun phrase syntax, and semantic expansion based on Wor", "weights": [[8, 15, 1.0], [200, 204, 1.0]]}, "W11-0112": {"snippet": "...ting natural language semantics. However, it is an open question of how best to integrate it with uncertain, probabilistic knowledge, for example regarding word meaning. This paper describes the first...", "weights": [[8, 15, 1.0], [159, 163, 1.0]]}, "W18-1405": {"snippet": "... use natural language to express spatial relations between and across entities in the world with great facility, natural language systems have a facility that depends on that human facility. This posi...", "weights": [[8, 15, 1.0], [116, 123, 1.0]]}, "2020.peoples-1.7": {"snippet": "...n in Conversations\n\n\n Since language model pretraining to learn contextualized word representations has been proposed, pretrained language models have made success in many natural language processing ...", "weights": [[8, 21, 1.0], [82, 86, 1.0], [175, 182, 1.0]]}, "H91-1064": {"snippet": "...In a natural dialog, a considerable proportion of the utterances actually relate to the maintenance of the dialog itself rather than to furthering the task or goals motivating the conversation. For ex...", "weights": [[8, 15, 1.0]]}, "D19-1186": {"snippet": "...t at word-level or discourse-level with a flat model to alleviate this problem, we propose a hierarchical generation model to capture the different levels of diversity using the conditional variationa...", "weights": [[8, 12, 1.0]]}, "2019.sigirconf_workshop-2019birndl.11": {"snippet": "...ural word embeddings, which have reported good results in the latest years for several NLP tasks. In this work, we evaluate the performance of two popular neural word embeddings (Word2vec and GloVe) i...", "weights": [[8, 12, 1.0], [165, 169, 1.0]]}, "C92-1061": {"snippet": "...roof-Nets which is much related to Dependency Structures similar to those we meet in, for instance (Hudson 1984) . These new Proof-Nets are called Connection Nets. We show that Connection Nets provide...", "weights": [[8, 12, 1.0], [134, 138, 1.0], [161, 165, 1.0], [191, 195, 1.0]]}, "2019.gwc-1.7": {"snippet": "...ng a Word Sense Disambiguation System\n\n\n Arabic WordNet (AWN) represents one of the best-known lexical resources for the Arabic language. However, it contains various issues that affect its use in dif...", "weights": [[8, 12, 1.0]]}, "2018.ecir_conference-2018.12": {"snippet": "...cess natural language primarily on a lexico-syntactic level, thereby ignoring one of the most crucial components of human-to-human dialogue: its affective content. We take a step in this direction by ...", "weights": [[8, 15, 1.0]]}, "W10-4211": {"snippet": "...ting Conversations: a User Study\n\n\n In this paper we present a complete system for automatically generating natural language abstracts of meeting conversations. This system is comprised of components ...", "weights": [[8, 21, 1.0], [111, 118, 1.0], [149, 162, 1.0]]}, "2019.gwc-1.34": {"snippet": "...with Word-Net specifications. Besides presenting the representation effort, we show the utilization of OntoLex-Lemon to bridge from WordNet-like resources to full lexical descriptions and extend the c...", "weights": [[8, 12, 1.0]]}, "P14-1115": {"snippet": "...tten Conversations Based on Phrasal Queries\n\n\n We propose a novel abstractive querybased summarization system for conversations, where queries are defined as phrases reflecting a user information need...", "weights": [[8, 21, 1.0], [117, 130, 1.0]]}, "2019.wwwjournals_journal-ir0volumeA22A2.23": {"snippet": "... for natural language conversation\n\n\n AbstractAs an important task of artificial intelligence, natural language conversation has attracted wide attention of researchers in natural language processing....", "weights": [[8, 15, 1.0], [98, 105, 1.0], [175, 182, 1.0]]}, "2017.tois_journal-ir0volumeA35A3.10": {"snippet": "...s in natural language processing. Many neural network models such as Convolutional Neural Network (CNN), Recursive Neural Network (RNN), and Long Short-Term Memory (LSTM) have been proposed to learn r...", "weights": [[8, 15, 1.0]]}, "P17-2099": {"snippet": "...s of natural language. A generic, distributional representation of sentences that can capture the latent semantics is beneficial to multiple downstream applications. We observe a simple geometry of se...", "weights": [[8, 15, 1.0]]}, "2020.sigdial-1.10": {"snippet": "...tive Conversations using Latent Discourse Behaviors\n\n\n In this work, we study collaborative online conversations. Such conversations are rich in content, constructive and motivated by a shared goal. A...", "weights": [[8, 21, 1.0], [102, 115, 1.0], [122, 135, 1.0]]}, "K19-1066": {"snippet": "...ng a natural way of interaction between customers and adaptive intelligent systems. A key requirement in these systems is the ability to efficiently parse user queries, understand the intent behind ea...", "weights": [[8, 15, 1.0]]}, "2022.humeval-1.10": {"snippet": "...y of Word Sense Disambiguation for Naturalistic Social Dialogs in {A}merican {E}nglish\n\n\n Current evaluation practices for social dialog systems, dedicated to human-computer spontaneous conversation, ...", "weights": [[8, 12, 1.0]]}, "W12-4804": {"snippet": "...gram word-based language model, important word-to-word collocation information are lost during this compression and lead to a drop of the conversion accuracies. In order to overcome this problem, we p...", "weights": [[8, 12, 1.0], [45, 49, 1.0], [53, 57, 1.0]]}, "W06-3408": {"snippet": "...-art natural language processing components that have been modified to accommodate the unique nature of conversational data. In addition, we leverage the added richness of conversational data by analy...", "weights": [[8, 15, 1.0]]}, "2003.wwwconf_conference-2003.43": {"snippet": "...s of conversations exhibit unexpected behaviors. For example, there exists a composite e-service based on Mealy peers whose set of conversations is not context free (and not regular). (The set of conv...", "weights": [[8, 21, 1.0], [134, 147, 1.0], [199, 212, 1.0]]}, "2019.gwc-1.32": {"snippet": "...uate word embeddings in general; and (iii) comparing the performance in that task of the two word embeddings, extracted from the two WordNets. A better performance in that evaluation task results from...", "weights": [[8, 12, 1.0], [96, 100, 1.0]]}, "2006.cikm_conference-2006.32": {"snippet": "...g of natural language text. Motivated by the problem of building a noun phrase-level semantic parser and adapting it to various applications, such as machine translation and multilingual question answ...", "weights": [[8, 15, 1.0]]}, "N12-1012": {"snippet": "...e of Conversations?\n\n\n This study aims to infer the social nature of conversations from their content automatically. To place this work in context, our motivation stems from the need to understand how...", "weights": [[8, 21, 1.0], [72, 85, 1.0]]}, "2018.sigirconf_conference-2018.221": {"snippet": "...most natural way.\nTHE TUTORIALIn this tutorial, we start with a brief introduction to the recent progress on DL and RL that is related to natural language processing (NLP), information retrieval (IR) ...", "weights": [[8, 15, 1.0], [141, 148, 1.0]]}, "W10-4318": {"snippet": "...ized conversations. We investigated whether listeners perceive conversations with these improvements as natural (i.e., human-like) as human-human conversations. We also assessed their ability to corre...", "weights": [[8, 21, 1.0], [66, 79, 1.0], [107, 114, 1.0], [149, 162, 1.0]]}, "W93-0103": {"snippet": "...each word from corpus. The concept of a word is defined within the probabilistic framework. A variation of Belief Net named as Collocation Map is used to compute the probabilities. The Belief Net capt...", "weights": [[8, 12, 1.0], [43, 47, 1.0]]}, "R15-1061": {"snippet": "...rate Word Sense Representations\n\n\n Distributed representations of words have boosted the performance of many Natural Language Processing tasks. However, usually only one representation per word is obt...", "weights": [[8, 12, 1.0], [112, 119, 1.0], [192, 196, 1.0]]}, "N15-3017": {"snippet": "Brahmi-Net: A transliteration and script conversion system for languages of the {I}ndian subcontinent\n\n\n We present Brahmi-Net -an online system for transliteration and script conversion for all major...", "weights": []}, "2018.chiir_conference-2018.6": {"snippet": "...g in natural language\u00d0on messaging platforms such as Messenger, Skype, and Sina Weibo. Such conversational agents are attracting more investment, are gaining * Work carried out at Microsoft. Permissio...", "weights": [[8, 15, 1.0]]}, "C14-3002": {"snippet": "...ting Natural Languages\n\n\n Artificial neural networks are powerful statistical models that have been shown to provide excellent results in a number of domains. In the last few years, the computer visio...", "weights": [[8, 15, 1.0]]}, "C98-1063": {"snippet": "...th a natural language layer to convert English queries for use by the various search engines. The NI,P layer incorporates morphological analysis, noun phrase syntax, and semantic expansion based on Wo", "weights": [[8, 15, 1.0], [201, 205, 1.0]]}, "2020.emnlp-main.104": {"snippet": "...s\n\n\n Natural Language Processing (NLP) tasks are usually performed word by word on textual inputs. We can use arbitrary symbols to represent the linguistic meaning of a word and use these symbols as i...", "weights": [[8, 15, 1.0], [70, 74, 1.0], [78, 82, 1.0], [134, 143, 1.0], [172, 176, 1.0]]}, "2022.acl-tutorials.1": {"snippet": "...Deep Nets and Opportunities for the Future\n\n\n The first half of this tutorial will make deep nets more accessible to a broader audience, following \"Deep Nets for Poets\" and \"A Gentle Introduction to F...", "weights": [[8, 12, 1.0], [96, 100, 1.0], [156, 160, 1.0]]}, "2021.blackboxnlp-1.12": {"snippet": "Word Equations: Inherently Interpretable Sparse Word Embeddings through Sparse Coding\n\n\n Word embeddings are a powerful natural language processing technique, but they are extremely difficult to inter...", "weights": [[0, 4, 1.0], [48, 52, 1.0], [89, 93, 1.0], [120, 127, 1.0]]}, "P19-1324": {"snippet": "...ons (word embeddings) which are then put in context in the hidden layers. Since words are often ambiguous, representing the contextually relevant information is not trivial. We investigate how an LSTM...", "weights": [[8, 12, 1.0]]}, "2007.cikm_conference-2007.134": {"snippet": "...ls a word from a bigram connecting two words, and assigns a reasonable probability to words as a whole. It is more powerful than HMM to decode words containing over two characters. We conduct a compre...", "weights": [[8, 12, 1.0]]}, "2007.sigirconf_conference-2007.3": {"snippet": "...uent word in a document is a good indicator of an important topic in the document, so if the query uses the word, the document is likely to be relevant to your information need.One very simple word we...", "weights": [[8, 12, 1.0], [111, 115, 1.0], [196, 200, 1.0]]}, "W18-3016": {"snippet": "...s to represent lexical semantics. While a unified account of the lexical meaning relies on one being able to convert between these representations, in both directions, the conversion direction from se...", "weights": [[8, 17, 1.0]]}, "2014.cikm_conference-2014.122": {"snippet": "...tain word representations enhanced by the knowledge graph. Experiments on popular text mining and natural language processing tasks, including analogical reasoning, word similarity, and topic predicti...", "weights": [[8, 12, 1.0], [101, 108, 1.0], [168, 172, 1.0]]}, "Q16-1033": {"snippet": "...ling Conversations: An Application of Natural Language Processing to Mental Health\n\n\n Mental illness is one of the most pressing public health issues of our time. While counseling and psychotherapy ca...", "weights": [[8, 21, 1.0], [41, 48, 1.0]]}, "2011.wwwconf_conference-2011.78": {"snippet": "...s in conversations tend to converge to one another's communicative behavior: they coordinate in a variety of dimensions including choice of words, syntax, utterance length, pitch and gestures. In its ...", "weights": [[8, 21, 1.0]]}, "2020.lrec-1.94": {"snippet": "...e in Natural Dialogue\n\n\n The problem of building a coherent and non-monotonous conversational agent with proper discourse and coverage is still an area of open research. Current architectures only tak...", "weights": [[8, 15, 1.0]]}, "2020.jasis_journal-ir0volumeA71A5.4": {"snippet": "Joint Modeling of Characters, Words, and Conversation Contexts for Microblog Keyphrase Extraction", "weights": []}, "C16-1130": {"snippet": "...ised Word Sense Disambiguation with Neural Models\n\n\n Determining the intended sense of words in text -word sense disambiguation (WSD) -is a longstanding problem in natural language processing. Recentl...", "weights": [[8, 12, 1.0], [105, 109, 1.0], [167, 174, 1.0]]}, "2018.sigirconf_workshop-2018birndl.4": {"snippet": "...late word importance scores for each target document using a word-level random walk. Next, we optimize sentence embedding vectors using a Siamese neural network. Here, we utilize localized sentence re...", "weights": [[8, 12, 1.0], [64, 68, 1.0]]}, "D13-1096": {"snippet": "...Text Conversations\n\n\n Natural language conversation is widely regarded as a highly difficult problem, which is usually attacked with either rule-based or learning-based models. In this paper we propos...", "weights": [[8, 21, 1.0], [25, 32, 1.0]]}, "2018.sigirconf_workshop-2018birndl.7": {"snippet": "...date word selection Keyphrase formation Candidate word scoring Suffering overgeneration error (a) Classic Scheme Candidate phrase selection Keyphrase ranking", "weights": [[8, 12, 1.0], [53, 57, 1.0]]}, "S16-2023": {"snippet": "...that represent individual speakers. We show that such 'person vectors' are informative to others, and they influence basic patterns of communication like the choice of one's interlocutor in conversati...", "weights": [[8, 17, 1.0]]}, "J75-4028": {"snippet": "... for Word Concepts\n\n\n V a r i c u s representations have been used to partray the ribmiqs of mrd (mtably action) mncepts. The mst pruninent of these include decanp3sition trees, 1 -repreentations such...", "weights": [[8, 12, 1.0]]}, "2017.sigirconf_workshop-2017birndl2.2": {"snippet": "... 250 word based on the identified sentences in the reference paper from its cited text spans using maximal marginal relevance.", "weights": [[8, 12, 1.0]]}, "2016.cikm_conference-2016.74": {"snippet": "...s in word embedding have shown that semantic representations for words can be efficiently learned by distributional models. A natural generalization is then to represent both queries and documents as ...", "weights": [[8, 12, 1.0], [129, 136, 1.0], [163, 172, 1.0]]}, "2004.clef_workshop-2004.53": {"snippet": "... the Natural Language Processing techniques we had already developed for single word conflation. In addition, the question analysis is based on complex pattern matching either over forms, part-of-spee...", "weights": [[8, 15, 1.0], [83, 87, 1.0]]}, "P18-1091": {"snippet": "... for Natural Language Inference\n\n\n Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), is one of the most important problems in natural language processing. It requir...", "weights": [[8, 15, 1.0], [38, 45, 1.0], [165, 172, 1.0]]}, "2020.wsdm_workshop-2020privatenlp.6": {"snippet": "...pted Word Embeddings using Recurrent Neural Networks", "weights": [[8, 12, 1.0]]}, "2020.wsdm_workshop-2020privatenlp.10": {"snippet": "...d by word embedding models. We present a privacy proof that satises d-privacy where the privacy parameter provides guarantees with respect to a distance metric dened by the word embedding space. We de...", "weights": [[8, 12, 1.0], [176, 180, 1.0]]}, "P93-1034": {"snippet": "...of a word are formed from entries of its near lexical neighbors. A dimensionality reduction creates a space representing the syntactic categories of unambiguous words. A neural net trained on these sp...", "weights": [[8, 12, 1.0]]}, "2011.wsdm_conference-2011.66": {"snippet": "... the natural conversational nature of social messaging systems. Two of the salient features of the proposed approach are (i) an efficient localitybased clustering approach for identifying crowds of us...", "weights": [[8, 15, 1.0]]}, "2017.sigirconf_conference-2017.40": {"snippet": "... the word distributions across the corpus to assign a topic to each word occurrence. ese two paradigms are complementary in how they represent the meaning of word occurrences. While some previous work...", "weights": [[8, 12, 1.0], [71, 75, 1.0], [136, 145, 1.0], [161, 165, 1.0]]}, "2020.acl-main.127": {"snippet": "...uman conversations contain many types of information, e.g., knowledge, common sense, and language habits. In this paper, we propose a conversational word embedding method named PR-Embedding, which uti...", "weights": [[8, 21, 1.0], [152, 156, 1.0]]}, "E83-1021": {"snippet": "... (Sl-Nets) paradigm. The operations are reduced to functions of a fo~al language, thus changing the level of abstraction of the operations to be performed on SI-Nets. In this sense, operations on SI-N...", "weights": [[8, 12, 1.0], [164, 168, 1.0], [202, 206, 1.0]]}, "C80-1002": {"snippet": "Automatic Processing of Written {F}rench Language\n\n\n An automatic processor of written French language is described. This processor uses syntactic and semantic informations about words in order to con...", "weights": []}, "J77-4003": {"snippet": "...ntic Nets\n\n\n This paper presents an analysis in a semantic net formalism of the semantic structure of English sentences containing references to spatial-location. Spatial reference, hereafter -SR, pro...", "weights": [[8, 12, 1.0]]}, "2020.wwwconf_conference-2020c.2": {"snippet": "...duce word category information via the auxiliary word category label predicting task. To train our model, we construct a new Chinese query rewriting dataset and conduct experiments on it. The experime...", "weights": [[8, 12, 1.0], [52, 56, 1.0]]}, "2021.emnlp-main.403": {"snippet": "...t of natural conversation is to tailor one's response such that it accounts for concepts that the speaker and listener may or may not know and the contextual relevance of all prior concepts used in co...", "weights": [[8, 15, 1.0]]}, "W03-1403": {"snippet": "...y to Represent Metaphors in {W}ord{N}ets? Insights from the {H}amburg Metaphor Database\n\n\n This paper addresses the question whether metaphors can be represented in Word-Nets. For this purpose, domain...", "weights": [[8, 17, 1.0], [168, 172, 1.0], [173, 177, 1.0]]}, "W19-7510": {"snippet": "... the word. This allows a thorough understanding of the words, and also the knowledge they embody. Vaijayant\u012bko\u015ba is a popular Sanskrit lexicon that contains words from spoken language as well those us...", "weights": [[8, 12, 1.0]]}, "S19-2056": {"snippet": "...n in conversations through Tweets, {CNN} and {LSTM} deep neural networks\n\n\n Emotion detection from user-generated contents is growing in importance in the area of natural language processing. The appr...", "weights": [[8, 21, 1.0], [166, 173, 1.0]]}, "J18-4008": {"snippet": "...arns word distributions to represent: (1) different roles of conversational discourse, and (2) various latent topics in reflecting content information. By explicitly distinguishing the probabilities o...", "weights": [[8, 12, 1.0], [30, 39, 1.0]]}, "W06-2105": {"snippet": "...atic natural language understanding. We present an approach towards PP interpretation as part of a natural language understanding system which has been successfully employed in various NLP tasks for i...", "weights": [[8, 15, 1.0], [102, 109, 1.0]]}, "S17-1009": {"snippet": "...h in natural language processing but its usefulness is somewhat limited by its relatively small lexical coverage. The Paraphrase Database (PPDB) covers 650 times more words, but lacks the semantic str...", "weights": [[8, 15, 1.0]]}, "2020.acl-main.166": {"snippet": "...s to represent \"what to say\" and \"how to say\", and edges to represent natural transition between a message (the last utterance in a dialog context) and its response. We then present a novel CG grounde...", "weights": [[8, 17, 1.0], [63, 72, 1.0], [73, 80, 1.0]]}, "L18-1462": {"snippet": "...el a natural conversation with machines. However, largescale conventional dialogue corpora are mainly built for specified tasks (e.g., task-oriented systems for restaurant or bus information navigatio...", "weights": [[8, 15, 1.0]]}, "2021.naacl-main.246": {"snippet": "...gh a natural conversation. We explore one promising direction for conversational recommendation: mapping a conversational user, for whom there is limited or no data available, to most similar external...", "weights": [[8, 15, 1.0]]}, "2020.coling-main.108": {"snippet": "...tal. Word and Sentence embeddings are one of the most common starting points of any NLP task. A word embedding represents a given word in a predefined vector-space while maintaining vector relations w...", "weights": [[8, 12, 1.0], [99, 103, 1.0], [133, 137, 1.0]]}, "1996.tois_journal-ir0volumeA14A4.1": {"snippet": "...etri nets-namely, the so-called NR/T-nets (Nested-Relation/ Transition Nets). In NR/T-nets, the structural part is modeled by nested relations, and the behavioral part is modeled by a novel Petri net ...", "weights": [[8, 12, 1.0], [40, 44, 1.0], [74, 78, 1.0], [89, 93, 1.0]]}, "W17-5535": {"snippet": "...s in Natural Language Processing that have been shown to be mutually beneficial. In this work, we design and compare two Neural models for jointly learning both tasks. In the proposed approach, we fir...", "weights": [[8, 15, 1.0]]}}} -{"dataset": "ir-lab-sose-2024/ir-acl-anthology-20240504-training", "qid": "11", "default_text": "Algorithm acceleration with Nvidia CUDA", "runs": [{"name": "ir-lab-sose-2024/tira-ir-starter/BM25 (tira-ir-starter-pyterrier)", "P@10": 0.2, "R@100": 0.8, "nDCG@10": 0.46, "Judged@10": 0.4, "relevance": ["1", "U", "U", "U", "U", "1", "0", "U", "0", "U", "0"], "docs": [{"doc_id": "2012.cikm_conference-2012.161", "doc_id_to_offset": {"start": 171855988, "end": 171859075}}, {"doc_id": "D18-2023", "doc_id_to_offset": {"start": 27996187, "end": 27997473}}, {"doc_id": "N18-3013", "doc_id_to_offset": {"start": 28711604, "end": 28712699}}, {"doc_id": "C00-2132", "doc_id_to_offset": {"start": 96874052, "end": 96875264}}, {"doc_id": "W11-2921", "doc_id_to_offset": {"start": 103697757, "end": 103699977}}, {"doc_id": "2009.cikm_conference-2009.70", "doc_id_to_offset": {"start": 173991803, "end": 173994473}}, {"doc_id": 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