Cross-Sentence N-ary Relation Extraction with Graph LSTMs
Table 1: Average test accuracy in five-fold cross-validation for drug-gene-mutation ternary interactions. Feature-Based used the best performing model in [Quirk and Poon2017] with features derived from shortest paths between all entity pairs.
['[BOLD] Model', '[BOLD] Single-Sent.', '[BOLD] Cross-Sent.']
All neural-network based models outperformed the feature-based classifier, illustrating their advantage in handling sparse linguistic patterns without requiring intense feature engineering. All LSTMs significantly outperformed CNN in the cross-sentence setting, verifying the importance in capturing long-distance dependencies.