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Named Entity Recognition on CoNLL English 2003 (test)

94.6F1 Score

ACE

Updated 3d ago

Evaluation Results

Method
ACE 2020.10	94.6	-	-	-	-
Yamada et al. (2020) 2020.11	94.3	-	-	-	-
Yamada et al. 2020.10	94.3	-	-	-	-
XLM-R 2020.10	94.1	-	-	-	-
Transformer-Linear (+DEV) 2020.11	94.09	-	-	-	-
Transformer-Linear 2020.11	93.75	-	-	-	-
Transformer-Linear 2020.11	93.64	-	-	-	-
Baevski et al. 2019.11	93.5	-	-	-	-
Yu et al. (2020) 2020.11	93.5	-	-	-	-
Baevski et al. 2020.10	93.5	-	-	-	-
Yu et al. 2020.10	93.5	-	-	-	-
Yu et al. (2020) 2021.05	93.5	-	-	93.7	93.3
Straková et al. 2020.10	93.4	-	-	-	-
LSTM-CRF 2019.08	93.38	-	-	-	-
Straková et al. (2019) 2020.11	93.38	-	-	-	-
Hire-NER + BERT 2019.11	93.37	-	-	-	-
BERT-MRC+DSC 2019.11	93.33	-	-	93.41	93.25
Liu et al. 2019b (BERT) 2019.11	93.23	-	-	-	-
LSTM-CRF 2019.08	93.22	-	-	-	-
Akbik, Blythe, and Vollgraf 2019.11	93.2	-	-	-	-
Akbik, Bergmann, and Vollgraf 2019 2019.11	93.18	-	-	-	-
Akbik et al. (2019b) 2020.11	93.18	-	-	-	-
BERT-MRC+DL 2019.11	93.17	-	-	93.22	93.12
LSTM-CRF (all layer mean) 2020.11	93.12	-	-	-	-
BERT-MRC+FL 2019.11	93.11	-	-	93.13	93.09
Flair 2019.08	93.09	-	-	-	-
Akbik, Blythe, and Vollgraf 2018 2019.11	93.09	-	-	-	-
seq2seq 2019.08	93.07	-	-	-	-
BERT-MRC 2019.11	93.04	-	-	92.33	94.61
BERT-MRC 2019.10	93.04	-	-	92.33	94.61
seq2seq 2019.08	93	-	-	-	-
seq2seq 2019.08	92.99	-	-	-	-
seq2seq 2019.08	92.98	-	-	-	-
LSTM-CRF 2019.08	92.94	-	-	-	-
Locate and Label 2021.05	92.94	-	-	92.13	93.73
LSTM-CRF 2019.08	92.93	-	-	-	-
BERT 2019.08	92.8	-	-	-	-
Devlin et al. 2019 (BERT) 2019.11	92.8	-	-	-	-
BERT-Tagger 2019.11	92.8	-	-	-	-
Devlin et al. (2019) 2021.05	92.8	-	-	-	-
BERT-Tagger 2019.10	92.8	-	-	-	-
Transformer-Linear 2020.11	92.79	-	-	-	-
Flair 2021.01	92.7	-	-	-	-
Clark et al. 2018 2019.11	92.61	-	-	-	-
Devlin et al. 2019.11	92.6	-	-	-	-
CVT 2019.11	92.6	-	-	-	-
CVT 2019.10	92.6	-	-	-	-
LSTM-CRF 2019.08	92.58	-	-	-	-
seq2seq 2019.08	92.43	-	-	-	-
Neural-CRF+AE 2018.08	92.29	-	-	-	-
LSTM-CRF 2019.08	92.25	-	-	-	-
Peters et al. (2018)+ELMo 2018.08	92.22	-	-	-	-
ELMo 2019.08	92.22	-	-	-	-
ELMo 2019.11	92.22	-	-	-	-
Peters et al. (2018) 2021.05	92.22	-	-	-	-
ELMO 2019.10	92.22	-	-	-	-
Peters et al. 2018 (ELMo) 2019.11	92.2	-	-	-	-
Flair-ML 2019.11	92.2	-	-	-	-
Trankit 2021.01	92.1	-	-	-	-
Stanza 2021.01	92.1	-	-	-	-
Hire-NER 2019.11	91.96	-	-	-	-
Neural-CRF+AE 2018.08	91.89	-	-	-	-
seq2seq 2019.08	91.87	-	-	-	-
LSTM-CRF (all layer mean) 2020.11	91.83	-	-	-	-
Qian et al. 2019 2019.11	91.74	-	-	-	-
Liu et al. 2018 2019.11	91.71	-	-	-	-
LSTM-CRF+char-IntNet-5 2018.10	91.64	-	-	-	-
Xin et al. 2018 2019.11	91.64	-	-	-	-
Yang et al. (2017) 2018.08	91.62	-	-	-	-
Chiu and Nichols (2016) 2018.08	91.62	-	-	-	-
Yang, Zhang, and Dong 2017 2019.11	91.62	-	-	-	-
Chiu and Nichols 2016 2019.11	91.62	-	-	-	-
Zhang, Liu, and Song 2018 2019.11	91.57	-	-	-	-
Liu et al. 2019b 2019.11	91.54	-	-	-	-
Chen et al. 2019 2019.11	91.44	-	-	-	-
LSTM-CRF+char-IntNet-9 2018.10	91.39	-	-	-	-
Yang and Zhang 2018 2019.11	91.35	-	-	-	-
BERT-ML 2019.11	91.3	-	-	-	-
Liu et al. 2018 2019.11	91.24	-	-	-	-
Ma and Hovy (2016) 2018.08	91.21	-	-	-	-
LSTM-CRF+char-CNN 2018.10	91.21	-	-	-	-
Ma and Hovy 2016 2019.11	91.21	-	-	-	-
CNN-BiLSTM-CRF (Ma and Hovy, 2016) 2019.11	91.21	-	-	-	-
Ma and Hovy (2016) 2019.06	91.21	-	-	-	-
Luo et al. (2015) 2018.08	91.2	-	-	-	-
GRM-CRF+char-GRU 2018.10	91.2	-	-	-	-
BERT-SL 2019.11	91.2	-	-	-	-
LSTM-CRF+char-LSTM 2018.10	91.13	-	-	-	-
LSTM-CRF+char-CNN 2018.10	91.11	-	-	-	-
BiLSTM-BiLSTM-CRF (Akhundov et al., 2018) 2019.11	91.11	-	-	-	-
Liu et al. 2019a 2019.11	91.1	-	-	-	-
CN3 2019.11	91.1	-	-	-	-
Neural-CRF 2018.08	91.06	-	-	-	-
BiLSTM-CRF 2019.10	91.03	-	-	-	-
Lample et al. (2016) 2018.08	90.94	-	-	-	-
LSTM-CRF+char-LSTM 2018.10	90.94	-	-	-	-
Lample et al. 2019.08	90.94	-	-	-	-
Lample et al. 2016 2019.11	90.94	-	-	-	-
BiLSTM-BiLSTM-CRF (Lample et al., 2016) 2019.11	90.94	-	-	-	-
Lample et al. (2016) 2019.06	90.94	-	-	-	-

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