Neural Reranking for Named Entity Recognition
Table 3: F1-value and SSA on test set.
['[BOLD] Model (%)', '[BOLD] F1', 'Δ [BOLD] F1', '[BOLD] SSA', 'Δ [BOLD] SSA']
Compared with the baseline, entities with type of PER and ORG receive the most improvements, showing that sentence patterns are useful for those types. While the performance on entities with type of MISC decreases slightly, since the MISC includes various entity types which bring noise on learning sentence patterns. We believe that our model will benifit more from the NER corpus with fine-grained entity type. The word based LSTM reranker achieves the F1-value of 88.75%, with 0.6% absolute improvement over the baseline tagger. Cooperating with CNN features on word only does not make much improvement, while character CNN features are more effective (+0.78%). However, the full combination of character representation and word CNN features improves the F-value to 89.25% (+1.10%) with the significance level of p<0.05 with t-test. The trend of SSA is the same as the F1-value, the accuracy is improved from the baseline 83.31% to 85.12% using the full model reranker, with an absolute improvement of 1.82%.