From dc600281c605f9f58b800d48c5f6106b1db19157 Mon Sep 17 00:00:00 2001 From: yingweixin Date: Sat, 18 Jan 2020 01:20:08 -0800 Subject: [PATCH] add NER state-of-the-art result (#296) Add NER state-of-the-art result with EMNLP 2018 paper (Learning Better Internal Structure of Words for Sequence Labeling), this paper proposed IntNet, which mainly focus on learning better character-to-word representations, IntNet significantly outperformed other character embedding models, and also combined with BiLSTM-CRF achieved state-of-the-art NER result without using any lexical features, transfer learning, language modeling or pre-training. --- english/named_entity_recognition.md | 1 + 1 file changed, 1 insertion(+) diff --git a/english/named_entity_recognition.md b/english/named_entity_recognition.md index b23fee02..9405651c 100644 --- a/english/named_entity_recognition.md +++ b/english/named_entity_recognition.md @@ -28,6 +28,7 @@ corpus tagged with four different entity types (PER, LOC, ORG, MISC). Models are | Peters et al. (2017) ♦| 91.93 | [Semi-supervised sequence tagging with bidirectional language models](https://arxiv.org/abs/1705.00108) | | | CRF + AutoEncoder (Wu et al., 2018) | 91.87 | [Evaluating the Utility of Hand-crafted Features in Sequence Labelling](http://aclweb.org/anthology/D18-1310) | [Official](https://github.com/minghao-wu/CRF-AE) | | Bi-LSTM-CRF + Lexical Features (Ghaddar and Langlais 2018) | 91.73 | [Robust Lexical Features for Improved Neural Network Named-Entity Recognition](https://arxiv.org/pdf/1806.03489.pdf) | [Official](https://github.com/ghaddarAbs/NER-with-LS) | +| BiLSTM-CRF + IntNet (Xin et al., 2018) | 91.64 | [Learning Better Internal Structure of Words for Sequence Labeling](https://www.aclweb.org/anthology/D18-1279) | | | Chiu and Nichols (2016) ♦| 91.62 | [Named entity recognition with bidirectional LSTM-CNNs](https://arxiv.org/abs/1511.08308) | | | HSCRF (Ye and Ling, 2018)| 91.38 | [Hybrid semi-Markov CRF for Neural Sequence Labeling](http://aclweb.org/anthology/P18-2038) | [HSCRF](https://github.com/ZhixiuYe/HSCRF-pytorch) | | IXA pipes (Agerri and Rigau 2016) | 91.36 | [Robust multilingual Named Entity Recognition with shallow semi-supervised features](https://doi.org/10.1016/j.artint.2016.05.003)| [Official](https://github.com/ixa-ehu/ixa-pipe-nerc)|