A Bi-LSTM-RNN Model for Relation ClassificationUsing Low-Cost Sequence Features
TABLE IV: Feature Contributions (%) in BioNLP-ST 2016 Task BB3. Here “+” means only one kind of features is added.
['[BOLD] Features', '[BOLD] F1', '[BOLD] Recall', '[BOLD] Precision']
Our model obtains 41.3% in F1 using only pre-trained word features. WordNet hypernym features are the most effective features, improving F1 from 41.3% to 44.8%. Random word features are more helpful than character features. POS features are less effective than any other kind of features, improving F1 by 0.6%.