Abstract
Past work in relation extraction has focused on binary relations in single sentences. Recent NLP inroads in high-value domains have sparked interest in the more general setting of extracting n-ary relations that span multiple sentences. In this paper, we explore a general relation extraction framework based on graph long short-term memory networks (graph LSTMs) that can be easily extended to cross-sentence n-ary relation extraction. The graph formulation provides a unified way of exploring different LSTM approaches and incorporating various intra-sentential and inter-sentential dependencies, such as sequential, syntactic, and discourse relations. A robust contextual representation is learned for the entities, which serves as input to the relation classifier. This simplifies handling of relations with arbitrary arity, and enables multi-task learning with related relations. We evaluate this framework in two important precision medicine settings, demonstrating its effectiveness with both conventional supervised learning and distant supervision. Cross-sentence extraction produced larger knowledge bases. and multi-task learning significantly improved extraction accuracy. A thorough analysis of various LSTM approaches yielded useful insight the impact of linguistic analysis on extraction accuracy.
Highlights
Relation extraction has made great strides in newswire and Web domains
The advent of $1000 human genome1 heralds the dawn of precision medicine, but progress in personalized cancer treatment has been hindered by the arduous task of interpreting genomic data using prior knowledge
We explore a general framework for cross-sentence n-ary relation extraction, based on graph long short-term memory networks
Summary
Relation extraction has made great strides in newswire and Web domains. Recently, there has. Tumors with L858E mutation in EGFR gene can be treated with gefitinib Extracting such knowledge clearly requires moving beyond binary relations and single sentences. We explore a general framework for cross-sentence n-ary relation extraction, based on graph long short-term memory networks (graph LSTMs). By adopting the graph formulation, our framework subsumes prior approaches based on chain or tree LSTMs, and can incorporate a rich set of linguistic analyses to aid relation extraction. We conducted extensive experiments on two important domains in precision medicine In both distant supervision and supervised learning settings, graph LSTMs that encode rich linguistic knowledge outperformed other neural network variants, as well as a well-engineered feature-based classifier. In the molecular tumor board domain, PubMedscale extraction using distant supervision from a small set of known interactions produced orders of magnitude more knowledge, and cross-sentence extraction tripled the yield compared to single-sentence extraction. Manual evaluation verified that the accuracy is high despite the lack of annotated examples
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