实体命名识别（NER）如何入门？

分享一些NER的论文，从F1-Score、Wordunit、Word Emb、Charunit、Crf、Note、Efficiency、Gazetters和LM多个视角对比了14篇NER论文。14篇论文如下：

1、【LSTM-CRF (Lample et al., 2016)】 Bidirectional LSTM-CRF for Clinical Concept Extraction

2、【Ma and Hovy (2016)】 End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF

3、【Transfer Learning Yang et al. (2017)】 Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks

4、【NCRF++ (Yang and Zhang, 2018)】 NCRF++ An Open-source Neural Sequence Labeling Toolkit

5、【HSCRF (Ye and Ling, 2018)】 Hybrid semi-Markov CRF for Neural Sequence Labeling

6、【Chiu and Nichols (2016.6.19)】 Named Entity Recognition with Bidirectional LSTM-CNNs

7、【LM-LSTM-CRF (Liu et al., 2017)】Empower sequence labeling with task-aware neural language model

8、【Bi-LSTM-CRF + Lexical Features】Contextualized Word Representations from Distant Supervision with and for NER

9、【CRF + AutoEncoder (Wu et al., 2018)】Evaluating the Utility of Hand-crafted Features in Sequence Labelling

10、【TagLM】Semi-supervised sequence tagging with bidirectional language models

11、【BiLSTM -CRF+ ELMo】Deep contextualized word representations

12、【BERT Base (Devlin et al., 2018.10.11)】BERT Pre-training of Deep Bidirectional Transformers for Language Understanding

13、【CVT + Multi-Task】Semi-Supervised Sequence Modeling with Cross-View Training

14、【Flair embeddings (Akbik et al., 2018)】FLAIR An Easy-to-Use Framework for State-of-the-Art NLP

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