Part-of-Speech Tagging on WSJ (test)
97.78AccuracyLing et al.
Evaluation Results
| Method | Links | ||
|---|---|---|---|
| Ling et al.2017.11 | 97.78 | — | |
| LM-LSTM-CRFType=maximum, pre-trained word embedding=true, leverage language models=true2017.09 | 97.59 | — | |
| Adversarial POS Taggingmode=adversarial training2017.11 | 97.58 | — | |
| Ma and HovyYear=20162018.06 | 97.55 | — | |
| Yang et al.Year=20172018.06 | 97.55 | — | |
| LM-LSTM-CRFType=reported, pre-trained word embedding=true, leverage language models=true2017.09 | 97.55 | — | |
| Ma and Hovy2017.11 | 97.55 | — | |
| Yang et al.2017.11 | 97.55 | — | |
| Hashimoto et al.2017.11 | 97.55 | — | |
| BiLSTM-CRFmode=baseline2017.11 | 97.54 | — | |
| LM-LSTM-CRFType=mean, pre-trained word embedding=true, leverage language models=true2017.09 | 97.53 | — | |
| Lample et al.Year=20162018.06 | 97.51 | — | |
| Lample et al. 2016Type=maximum, pre-trained word embedding=true2017.09 | 97.51 | — | |
| Søgaard 2011Type=reported2017.09 | 97.5 | — | |
| Søgaard2017.11 | 97.5 | — | |
| SrcEmbeddings=GloVe2018.04 | 97.5 | — | |
| CLSTM+WLSTM+CRFCharacter sequence representation=LSTM, Word sequence representation=LSTM, Inference layer=CRF2018.06 | 97.49 | — | |
| CCNN+WLSTM+CRFCharacter sequence representation=CNN, Word sequence representation=LSTM, Inference layer=CRF2018.06 | 97.46 | — | |
| Ma et al. 2016Type=maximum, pre-trained word embedding=true2017.09 | 97.46 | — | |
| TriEmbeddings=GloVe2018.04 | 97.45 | — | |
| Yang et al. 2017Type=reported, pre-trained word embedding=true2017.09 | 97.43 | — | |
| Stanford*2018.04 | 97.43 | — | |
| Ma et al. 2016Type=mean, pre-trained word embedding=true2017.09 | 97.42 | — | |
| CLSTM+WCNN+CRFCharacter sequence representation=LSTM, Word sequence representation=CNN, Inference layer=CRF2018.06 | 97.38 | — | |
| MT-TriEmbeddings=GloVe2018.04 | 97.37 | — | |
| Bi-LSTMReference=Ling et al., 20152016.04 | 97.36 | — | |
| Sun 2014Type=reported2017.09 | 97.36 | — | |
| Lample et al. 2016Type=mean, pre-trained word embedding=true2017.09 | 97.35 | — | |
| CCNN+WCNN+CRFCharacter sequence representation=CNN, Word sequence representation=CNN, Inference layer=CRF2018.06 | 97.33 | — | |
| ConvnetReference=Santos and Zadrozny, 20142016.04 | 97.32 | — | |
| Collobert et al. 2011Type=reported, pre-trained word embedding=true2017.09 | 97.29 | — | |
| Collobert et al.2017.11 | 97.29 | — | |
| Manning 2011Type=reported2017.09 | 97.28 | — | |
| Manning2017.11 | 97.28 | — | |
| Toutanova et al.2017.11 | 97.27 | — | |
| bi-LSTM (w+c)Input Representation=words (w) + characters (c), Epochs=30, sigma=0.3, Pre-training=no POLYGLOT2016.04 | 97.22 | — | |
| Nochar+WLSTM+CRFCharacter sequence representation=None, Word sequence representation=LSTM, Inference layer=CRF2018.06 | 97.2 | — | |
| AsymEmbeddings=GloVe2018.04 | 97.19 | — | |
| Rei 2017Type=max, pre-trained word embedding=true, leverage language models=true2017.09 | 97.14 | — | |
| FLORS*2018.04 | 97.11 | — | |
| Nochar+WCNN+CRFCharacter sequence representation=None, Word sequence representation=CNN, Inference layer=CRF2018.06 | 96.99 | — | |
| Rei 2017Type=mean, pre-trained word embedding=true, leverage language models=true2017.09 | 96.97 | — | |
| Context-to-Vecbackbone=BERT2022.10 | 96.91 | 92.8 | |
| BERT+Skip-gram2022.10 | 96.86 | 92.3 | |
| Convnet (reimplementation)Reference=Ling et al., 20152016.04 | 96.8 | — | |
| BERTavgpooling=average2022.10 | 96.8 | 90.76 | |
| SynGCN2022.10 | 96.71 | 92.23 | |
| TnT*2018.04 | 96.57 | — | |
| GloVe2022.10 | 96.52 | 91.84 | |
| Bi-RNNReference=Ling et al., 20152016.04 | 95.93 | — | |
| Skip-gram2022.10 | 95.12 | 89.03 |