【NLP】命名实体识别CoNLL2003语料公测排行榜（最新）

命名实体识别CoNLL2003

CoNLL 2003 data

https://www.clips.uantwerpen.be/conll2003/ner/

CoNLL 2003 (English)

The CoNLL 2003 NER task consists of newswire text from the Reuters RCV1
corpus tagged with four different entity types (PER, LOC, ORG, MISC). Models are evaluated based on span-based F1 on the test set.

CoNLL 2003 排行榜

最新进展

Model	F1	Paper / Source	Code
Flair embeddings (Akbik et al., 2018)	93.09	Contextual String Embeddings for Sequence Labeling	Flair framework
BERT Large (Devlin et al., 2018)	92.8	BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
CVT + Multi-Task (Clark et al., 2018)	92.61	Semi-Supervised Sequence Modeling with Cross-View Training	Official
BERT Base (Devlin et al., 2018)	92.4	BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
BiLSTM-CRF+ELMo (Peters et al., 2018)	92.22	Deep contextualized word representations	AllenNLP ProjectAllenNLP GitHub
Peters et al. (2017)	91.93	Semi-supervised sequence tagging with bidirectional language models
HSCRF (Ye and Ling, 2018)	91.38	Hybrid semi-Markov CRF for Neural Sequence Labeling	HSCRF
NCRF++ (Yang and Zhang, 2018)	91.35	NCRF++: An Open-source Neural Sequence Labeling Toolkit	NCRF++
LM-LSTM-CRF (Liu et al., 2018)	91.24	Empowering Character-aware Sequence Labeling with Task-Aware Neural Language Model	LM-LSTM-CRF
Yang et al. (2017)	91.26	Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks
Ma and Hovy (2016)	91.21	End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF
LSTM-CRF (Lample et al., 2016)	90.94	Neural Architectures for Named Entity Recognition

 

转载自：https://yuanxiaosc.github.io/2018/12/26/%E5%91%BD%E5%90%8D%E5%AE%9E%E4%BD%93%E8%AF%86%E5%88%ABCoNLL2003/