Self-Attention Gazetteer Embeddings for Named-Entity Recognition
Table 3: Ablation study results on CoNLL-03 and OntoNotes 5. “- span encoding” removes the BILU match span encoding leaving only the gazetteer types. “- self attention” removes the self-attention. “- uncased matches” removes the uncased matches.
['[BOLD] Model', '[BOLD] CoNLL-03 Dev F1±\xa0std', '[BOLD] CoNLL-03 Test F1±\xa0std', '[BOLD] Ontonotes 5 Dev F1±\xa0std', '[BOLD] Ontonotes 5 Test F1±\xa0std']
We remove components of the gazetteer embedding model from the Neural CRF model. In each experiment, we removed only the specified component. Ablations show decreased F1 score on the development and test set if any of the components is removed. The highest degradation is when single matches are removed which underscores the importance of the combining the gazetteer matching techniques for NER. We observe that match span encoding is more important for the CoNLL-02 compared to Ononotes 5 because the former has more entities with multiple tokens. Removing the self-attention shows that self-attention is effective at combining information form multiple gazetteers.