Cross-Sentence N-ary Relation Extraction with Graph LSTMs
Table 7: GENIA test results on the binary relation of gene regulation. Graph LSTM (GOLD) used gold syntactic parses in the document graph.
['[BOLD] Model', '[BOLD] Precision', '[BOLD] Recall', '[BOLD] F1']
Since the dataset is not entirely balanced, we reported precision, recall, and F1. We used our best performing graph LSTM from the previous experiments. By default, automatic parses were used in the document graphs, whereas in Graph LSTM (GOLD), gold parses were used instead. Graph LSTMs exhibited a more commanding advantage over linear-chain LSTMs in this domain, substantially outperforming the latter (p<0.01 by McNemar ’s chi-square test). Most interestingly, graph LSTMs using gold parses significantly outperformed that using automatic parses, suggesting that encoding high-quality analysis is particularly beneficial.