For fair comparison, we add three features (POS tags, NER tags and WordNet hypernyms of marked entities) to the vector extracted by our model and retrain the network.
our model achieves an F 1 of 84.6 and outperforms all existing baselines in a fair condition where no data augmentation strategy is adopted.