Leveraging Knowledge Bases in LSTMs for Improving Machine Reading
Table 1: Entity extraction results on the ACE2005 test set with gold-standard mention boundaries.
['[BOLD] Model', '[BOLD] P', '[BOLD] R', '[BOLD] F1']
We first report results on entity extraction with gold-standard boundaries for multi-word mentions. This allows us to focus on the performance of entity type prediction without considering mention boundary errors and the noise they introduce in retrieving candidate KB concepts. We find that the CRF objective generally outperforms the softmax objective. Our KBLSTM-CRF model significantly improves over its counterpart BiLSTM-Fea-CRF. This demonstrates the effectiveness of KBLSTM architecture in leveraging KB information.