Dependency-Guided LSTM-CRF for Named Entity Recognition
Table 3: Performance comparison on the OntoNotes 5.0 English dataset.
['[BOLD] Model', '[BOLD] Prec.', '[BOLD] Rec.', '[BOLD] F [BOLD] 1']
Without the LSTM layers (i.e., L=0), the proposed model with dependency information significantly improves the NER performance with more than 2 points in F1 compared to the baseline BiLSTM-CRF (L=0), which demonstrate the effectiveness of dependencies for the NER task. Li et al. This baseline also outperforms the CNN-based models Strubell et al. Li et al. The BiLSTM-GCN-CRF model outperforms the BiLSTM-CRF model but achieves inferior performance compared to the proposed DGLSTM-CRF model. We believe it is challenging to preserve the surrounding context information with stacking GCN layers while contextual information is important for NER Peters et al. Overall, the 2-layer DGLSTM-CRF model significantly (with p<0.01) outperforms the best BiLSTM-CRF baseline and the BiLSTM-GCN-CRF model. As we can see from the table, increasing the number of layers (e.g., L = 3) does not give us further improvements for both BiLSTM-CRF and DGLSTM-CRF because such third-order information (e.g., the relationship among a word’s parent, its grandparent, and great-grandparent) does not play an important role in indicating the presence of named entities.