Dependency-Guided LSTM-CRF for Named Entity Recognition
Table 6: Performance on the CoNLL-2003 English dataset.
['[BOLD] Model', '[BOLD] Prec.', '[BOLD] Rec.', '[BOLD] F [BOLD] 1']
The dependencies are predicted from Spacy Honnibal and Montani With the contextualized word representations, DGLSTM-CRF outperforms BiLSTM-CRF with 0.2 points in F1 (p<0.09). The improvement is not significant due to the relatively lower equality of the dependency trees. To further study the effect of the dependencies, we modified the predicted dependencies to ensure each entity form a subtree in the complete dataset. Such modification improves the F1 to 92.7, which is significantly better (p<0.05) than the BiLSTM-CRF.