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 crosssentence relation extraction . At the core of our approach is a graph representation 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 . 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 ( Bahcall , 2015 ) . The cost of sequencing a person 's genome has fallen below $ 10001 , enabling individualized diagnosis and treatment of complex genetic 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 extraction 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 particularly promising paradigm for augmenting existing knowledge bases from unlabeled text ( Poon et al . , 2015 ; Parikh et al . , 2015 ) . This progress is exciting , but distantsupervision approaches have so far been limited to single sentences , thus missing out on relations crossing the sentence boundary . Consider the following 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 not be inferred from either sentence alone . The impact of missed opportunities is especially pronounced in the long tail of knowledge . Such information is crucial for integrative reasoning as it includes the newest findings in specialized domains . In this paper , we present DISCREX , the first approach for distant supervision to relation extraction beyond the sentence boundary . The key idea is to adopt a document-level graph representation that augments conventional intra-sentential dependencies with new dependencies introduced for adjacent sentences and discourse relations . It provides a unifying way to derive features for classifying relations between entity pairs . As we augment this graph with new arcs , the number of possible paths between entities grow . We demonstrate that feature extraction along multiple paths leads to more robust extraction , allowing the learner to find structural patterns even when the language varies or the parser makes an error . The cross-sentence scenario presents a new challenge in candidate selection . This motivates our concept of minimal-span candidates in Section 3.2 . Excluding non-minimal candidates substantially improves classification accuracy . There is a long line of research on discourse phenomena , including coreference ( Haghighi and Klein , 2007 ; Poon and Domingos , 2008 ; Rahman and Ng , 2009 ; Raghunathan et al . , 2010 ) , narrative structures ( Chambers and Jurafsky , 2009 ; Cheung et al . , 2013 ) , and rhetorical relations ( Marcu , 2000 ) . For the most part , this work has not been connected to relation extraction . Our proposed extraction framework makes it easy to integrate such discourse relations . Our experiments evaluated the impact of coreference and discourse parsing , a preliminary step toward in-depth integration with discourse research . We conducted experiments on extracting druggene interactions from biomedical literature , an important task for precision medicine . By bootstrapping from a recently curated knowledge base ( KB ) with about 162 known interactions , our DIS-CREX system learned to extract inter-sentence drug-gene interactions at high precision . Crosssentence extraction doubled the yield compared to single-sentence extraction . Overall , by applying distant supervision , we extracted about 64,000 distinct interactions from about one million PubMed Central full-text articles , attaining two orders of magnitude increase compared to the original KB . We present the first approach for applying distant supervision to cross-sentence relation extraction , by adopting a document-level graph representation that incorporates both intra-sentential dependencies and inter-sentential relations such as adjacency and discourse relations . We conducted both automatic and manual evaluation on extracting drug-gene interactions from biomedical literature . With cross-sentence extraction , our DIS-CREX system doubled the yield of unique interactions , while maintaining the same accuracy . Using distant supervision , DISCREX improved the coverage of the Gene Drug Knowledge Database ( GDKD ) by two orders of magnitude , without requiring annotated examples . Future work includes : further exploration of features ; improved integration with coreference and discourse parsing ; combining distant supervision with active learning and crowd sourcing ; evaluate the impact of extractions to precision medicine ; applications to other domains .

Challenge: Existing distance supervision methods for relation extraction cannot capture relations crossing the sentence boundary which is important in specialized domains with long-tail knowledge.
Approach: They propose a method for applying distance supervision to cross-sentence relation extraction by adopting a document-level graph representation that incorporates intra-sentential dependencies and inter-sentential relations.
Outcome: Experiments on extracting drug-gene interactions from biomedical literature show that the proposed method doubles the performance of single-sentence extraction methods.