Dependency-Guided LSTM-CRF for Named Entity Recognition
Table 5: Results on the SemEval-2010 Task 1 datasets.
['[BOLD] Model', '[BOLD] Catalan  [BOLD] Prec.', '[BOLD] Catalan  [BOLD] Rec.', '[BOLD] Catalan  [BOLD] F [BOLD] 1', '[BOLD] Spanish  [BOLD] Prec.', '[BOLD] Spanish  [BOLD] Rec.', '[BOLD] Spanish  [BOLD] F [BOLD] 1']
Overall, we observe substantial improvements of the DGLSTM-CRF on the Catalan and Spanish datasets (with p<0.001 marked in bold against the best performing BiLSTM-CRF baseline), especially for DGLSTM-CRF with ELMo and L larger than 1. With word embeddings, the best DGLSTM-CRF model outperforms the best performing BiLSTM-CRF baseline with more than 10 and 9 points in F1 on the Catalan and Spanish datasets, respectively. The BiLSTM-GCN-CRF model also performs much better than the BiLSTM-CRF baselines but is worse than the DGLSTM-CRF model with L≥2. Both precision and recall significantly improve with a large margin compared to the best performing BiLSTM-CRF, especially for the recall (with more than 10 points improvement) on these two datasets. With ELMo, the best performing DGLSTM-CRF model outperforms the BiLSTM-CRF baseline with about 6 and 7 points in F1 on these two datasets, respectively. The substantial improvements show that the structural dependency information is extremely helpful for these two datasets.