Leveraging Knowledge Bases in LSTMs for Improving Machine Reading
Table 5: event extraction results on the ACE2005 test set.
['Model', 'P', 'R', 'F1']
4.2.1 Results We compare our models with the prior state-of-the-art approaches for event extraction, including neural and non-neural ones: JointBeam refers to the joint beam search approach with local and global features Li et al. The second block shows the previously reported results for the neural models. Note that they both make use of gold-standard entity annotations. The third block shows the results of our models. We can see that our KBLSTM models significantly outperform the BiLSTM and BiLSTM-Fea models, which again confirms their effectiveness in leveraging KB information. The KBLSTM-CRF model achieves the best performance and outperforms the previous state-of-the-art methods without having access to any gold-standard entities.