Nerwip v4

Named Entity Extraction in Wikipedia Pages

• Copyright 2011 (v1) Yasa Akbulut, Burcu Küpelioğlu & Vincent Labatut
• Copyright 2012 (v2) Burcu Küpelioğlu, Samet Atdağ & Vincent Labatut
• Copyright 2013 (v3) Samet Atdağ & Vincent Labatut
• Copyright 2014-15 (v4) Vincent Labatut

Nerwip is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation. For source availability and license information see licence.txt

• Lab site: http://lia.univ-avignon.fr
• GitHub repo: https://github.com/CompNet/Nerwip
• Contact: Vincent Labatut vincent.labatut@univ-avignon.fr

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Description

This platform was initially designed to apply and compare Named Entity Recognition (NER) tools on collections of texts. It was later specialized to process biographical articles extracted from the English version of Wikipedia. It allows using several standard standalone tools listed below, as well as some custom tools. It also implements several ways of combining the outputs of standalone NER tools to improve the global output.

The first results obtained on Wikipedia biographical texts were published in [AL'13]. If you use this software, please cite this reference:

@InProceedings{Atdag2013a,
  author    = {Atdağ, Samet and Labatut, Vincent},
  title     = {A Comparison of Named Entity Recognition Tools Applied to Biographical Texts},
  booktitle = {2nd International Conference on Systems and Computer Science},
  year      = {2013},
  pages     = {228-233},
  address   = {Lille, FR},
  doi       = {10.1109/IcConSCS.2013.6632052},
}

Organization

The source code takes the form of an Eclipse project. It is organized as follows:

• Package data contains all the classes used to represent data: articles, entities, etc.
• Package edition contains a secondary tool, allowing the annotation of articles and the visualization of annotated articles.
• Pacakge evaluation contains classes used to measure the performances of NER tools. Additional measures can be implemented (see the Extension section)
• Package recognition contains the NER-related source code. We distinguish different types of NER tools:
  • Combiners: tools combining the output of standalone tools
  • External: tools executed out of Nerwip, in this case the classes are basically just wrappers.
  • Internal: tools executed from within Nerwip. This requires some internal object-to-object conversion.
    • Model-based: some model (or other data) must be loaded before the NER tool is applied.
    • Model-less: no model needed (e.g. our too Subee, or the Web-service OpenCalais).
• Package retrieval contains classes used to get Wikipedia articles and constitute a collection of biogprahical texts.
• Package tools: various classes used throughout the software.

The rest of the files are resources:

• Folder lib contains the external libraries, especially the NER-related ones (cf. the Dependencies section).
• Folder log contains the log generated during the processing.
• Folder out contains the articles and the files generated during the process. On this GitHub repo, it is empty for space matters, but a corpus can be retrieved from FigShare.
• Folder res contains the XML schemas (XSD files) used by Nerwip, as well as the configuration files required by certain NER tools.

Installation

First, be sure to get the source code of a stable release by checking the release page of the GitHub repo. Second, you need to download some additional files to get the required data.

Most of the data files are too large to be compatible with GitHub constraints. For this reason, they are hosted on FigShare. Before using Nerwip, you need to retrieve these archives and unzip them in the Eclipse project.

1. Go to our FigShare page.
2. This GitHub project contains only a small part of our corpus. If you want the whole dataset, then:

• Download the v4 of the corpus as a Zip archive,
• Extract the out folder,
• Put it in the Eclipse project in place of the existing out folder.

3. You also need the data related to the different NER tools (models, dictionaries, etc.).

• Download all 4 Zip files containing the NER data,
• Extract the res folder,
• Put it in the Eclipse project, in place of the existing res folder. Do not remove the existing folder, just overwrite it (we need the existing folders and files).

Finally, some of the NER tools integrated in Nerwip require some key or password to work. This is the case of:

• Subee: our Wikipedia/Freebase-based NER tool requires a Freebase key to work correctly.
• OpenCalais: this NER tool takes the form of a Web service. All keys are set up in the dedicated XML file keys.xml, which is located in res/misc.

Use

The main objective of this project is the automatic annotation of entities. However, it also includes a tool allowing to easily perform the manual annotation of texts, in order to constitute a ground-truth used when evaluating the automatic tools.

Automatic Annotation

For now, there is not interface, not even a command-line one. All the processes need to be launched programmatically, as illustrated by class tr.edu.gsu.nerwip.Launch. I advise to import the project in Eclipse and directly edit the source code in this class. A more appropriate interface will be added once the software is more stable.

The output folder is out by default. This can be changed by editing the class tr.edu.gsu.nerwip.tools.file.FileNames (field FO_OUTPUT).

Manual Annotation

The project contains a graphical interface allowing the visualization of annotated texts, as well as the annotation of texts. This means this tool might be used to annotate new articles, to change existing annotations, or to visualy check the output of a NER tool. This annotation tool corresponds to the class tr.edu.gsu.nerwip.edition.EntityEditor, which can be directly launched.

Extension

It is relatively straightforward to extend our platform in order to include other NER tools (standalone or combination). For this purpose, one has to extend one of the classes tr.edu.gsu.nerwip.recognition.combiner.AbstractCombiner (to combine some NER tools already integrated in Nerwip), tr.edu.gsu.nerwip.recognition.internal.AbstractInternalRecognizer (for a NER tool taking the form of a Java program, and programmatically invocable directly from Nerwip), or tr.edu.gsu.nerwip.recognition.external.AbstractExternalRecognizer (for a NER tool whose only invocable through the command line). For standalone NER tools, it is also necessary to extend either tr.edu.gsu.nerwip.recognition.internal.AbstractInternalConverter (conversion between the NER tool Java objects and Nerwip's) or tr.edu.gsu.nerwip.recognition.external.AbstractExternalConverter (reads the file generated by the external NER tool and convert its content in Nerwip objects). Note new evaluation measures can also be defined by extending the class tr.edu.gsu.nerwip.evaluation.measure.AbstractMeasure.

Dependencies

Here are the dependencies for Nerwip:

• NER tools:
  • Illinois Named Entity Tagger
  • alias-i LingPipe
  • Apache OpenNLP
  • Stanford Named Entity Recognizer
  • Thomson Reuters OpenCalais
• Other libraries:
  • Apache Commons Loggging
  • HTML Parser
  • Apache HttpComponents
  • JDOM
  • Jericho HTML Parser
  • JSON Simple
  • Jsoup
  • LIBSVM

References

• [AL'13] S. Atdağ & V. Labatut. A Comparison of Named Entity Recognition Tools Applied to Biographical Texts, 2nd International Conference on Systems and Computer Science, 228-233, 2013. DOI: 10.1109/IcConSCS.2013.6632052 ⟨hal-01322867⟩ ⟨hal-00849797⟩
• [L'13] V. Labatut. Improved Named Entity Recognition Through SVM-Based Combination, Technical Report, Galatasaray University, 2013. ⟨hal-01322867⟩