Distant Supervision for Relation Extraction beyond the Sentence Boundary

Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 1171–1182, Valencia, Spain, April 3-7, 2017. c⃝2017 Association for Computational Linguistics Distant Supervision for Relation Extraction beyond the Sentence Boundary Chris Quirk and Hoifung Poon Microsoft Research One Microsoft Way Redmond, WA 98052 {chrisq,hoifung}@microsoft.com Abstract The growing demand for structured knowledge has led to great interest in relation extraction, especially in cases with limited supervision. However, existing distance supervision approaches only extract relations expressed in single sentences. In general, cross-sentence relation extraction is under-explored, even in the supervised-learning setting. In this paper, we propose the first approach for applying distant supervision to cross- sentence relation extraction. At the core of our approach is a graph representa- tion that can incorporate both standard dependencies and discourse relations, thus providing a unifying way to model relations within and across sentences. We extract features from multiple paths in this graph, increasing accuracy and robustness when confronted with linguistic variation and analysis error. Experiments on an important extraction task for precision medicine show that our approach can learn an accurate cross-sentence extractor, using only a small existing knowledge base and unlabeled text from biomedical research articles. Compared to the existing distant supervision paradigm, our approach extracted twice as many relations at similar precision, thus demonstrating the prevalence of cross-sentence relations and the promise of our approach. 1 Introduction The accelerating pace in technological advance and scientific discovery has led to an explosive growth in knowledge. The ensuing information overload creates new urgency in assimilating frag- mented knowledge for integration and reasoning. A salient case in point is precision medicine (Bah- call, 2015). The cost of sequencing a person’s genome has fallen below $10001, enabling indi- vidualized diagnosis and treatment of complex ge- netic diseases such as cancer. The availability of measurement for 20,000 human genes makes it imperative to integrate all knowledge about them, which grows rapidly and is scattered in millions of articles in PubMed2. Traditional extraction approaches require annotated examples, which makes it difficult to scale to the explosion of ex- traction demands. Consequently, there has been increasing interest in indirect supervision (Banko et al., 2007; Poon and Domingos, 2009; Toutanova et al., 2015), with distant supervision (Craven et al., 1998; Mintz et al., 2009) emerging as a partic- ularly promising paradigm for augmenting exist- ing knowledge bases from unlabeled text (Poon et al., 2015; Parikh et al., 2015). This progress is exciting, but distant- supervision approaches have so far been limited to single sentences, thus missing out on relations crossing the sentence boundary. Consider the fol- lowing example:“The p56Lck inhibitor Dasatinib was shown to enhance apoptosis induction by dexamethasone in otherwise GC-resistant CLL cells. This finding concurs with the observation by Sade showing that Notch-mediated resistance of a mouse lymphoma cell line could be overcome by inhibiting p56Lck.” Together, the two sentences convey the fact that the drug Dasatinib could overcome resistance conferred by mutations to the Notch gene, which can …

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