Relation Extraction with Word Graphs from N-grams
Han Qin, Yuanhe Tian, Yan Song · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · 2021
Most recent studies for relation extraction (RE) leverage the dependency tree of the input sentence to incorporate syntax-driven contextual information to improve model performance, with little attention paid to the limitation where high-quality dependency parsers in most cases unavailable, especially for indomain scenarios.To address this limitation, in this paper, we propose attentive graph convolutional networks (A-GCN) to improve neural RE methods with an unsupervised manner to build the context graph, without relying on the existence of a dependency parser.Specifically, we construct the graph from n-grams extracted from a lexicon built from pointwise mutual information (PMI) and apply attention over the graph.Therefore, different word pairs from the contexts within and across n-grams are weighted in the model and facilitate RE accordingly.Experimental results with further analyses on two English benchmark datasets for RE demonstrate the effectiveness of our approach, where state-of-the-art performance is observed on both datasets.1