Bidirectional LSTM-CRF Models for Sequence Tagging
Table 6: Comparison of F1 scores of different models for NER.
['System', 'accuracy']
They used a combination of various machine-learning classifiers. The second best performer of CoNLL 2003 Our model can achieve the best F1 score of 90.10 with both Senna embedding and gazetteer features. With the same Senna embedding, BI-LSTM-CRF slightly outperforms Conv-CRF (90.10% vs. 89.59%). However, BI-LSTM-CRF significantly outperforms Conv-CRF (84.26% vs. 81.47%) if random embedding is used.