Leveraging Knowledge Bases in LSTMs for Improving Machine Reading
Table 3: Entity extraction results on the ACE2005 test set.
['Model', 'P', 'R', 'F1']
Next, we evaluate our models on entity extraction with predicted mention boundaries. We then treat the predicted chunks as units for entity type labeling. Interestingly, we find that using BiLSTM-CRF without any KB information already gives strong performance compared to previous work. The KBLSTM-CRF model demonstrates the best performance among all the models and achieves the new state-of-the-art performance on the ACE2005 dataset.