A Bi-LSTM-RNN Model for Relation ClassificationUsing Low-Cost Sequence Features
TABLE II: Feature Contributions (%) in SemEval-2010 Task 8. Here “+” means only one kind of features is added.
['[BOLD] Features', '[BOLD] F1']
By using only pre-trained word features, our model can achieve 78.8% in F1. WordNet hypernym features are the most effective features, improving F1 from 78.8% to 79.8%. Character features are less effective than others, improving F1 by 0.5%.