Leveraging Knowledge Bases in LSTMs for Improving Machine Reading
Table 4: Entity extraction results on the OntoNotes 5.0 test set.
['Model', 'P', 'R', 'F1']
We compare our models with the existing feature-based models \newciteratinov2009design and \newcitedurrett2014joint, which both employ heavy feature engineering to bring in external knowledge. Our KBLSTM-CRF model shows competitive results compared to their results. It also presents significant improvements compared to the BiLSTM and BiLSTM-Fea models. Note that the benefit of adding KB information is smaller on OntoNotes compared to ACE2005. We believe that there are two main reasons. One is that NELL has a lower coverage of entity mentions in OntoNotes than in ACE2005 (57% vs. 65%). Second, OntoNotes has a significantly larger amount of training data, which allows for more accurate models without much help from external resources.