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        <title>Cross-Sentence N-ary Relation Extraction with Graph LSTMs</title>
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        <namePart type="given">Nanyun</namePart>
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    <name type="personal">
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        <namePart type="family">Toutanova</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.1162/tacl_a_00049</identifier>
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%0 Journal Article
%T Cross-Sentence N-ary Relation Extraction with Graph LSTMs
%A Peng, Nanyun
%A Poon, Hoifung
%A Quirk, Chris
%A Toutanova, Kristina
%A Yih, Wen-tau
%J Transactions of the Association for Computational Linguistics
%D 2017
%V 5
%I MIT Press
%C Cambridge, MA
%F peng-etal-2017-cross
%X 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.
%R 10.1162/tacl_a_00049
%U https://aclanthology.org/Q17-1008
%U https://doi.org/10.1162/tacl_a_00049
%P 101-115

Markdown (Informal)

[Cross-Sentence N-ary Relation Extraction with Graph LSTMs](https://aclanthology.org/Q17-1008) (Peng et al., TACL 2017)

• Cross-Sentence N-ary Relation Extraction with Graph LSTMs (Peng et al., TACL 2017)

ACL

• Nanyun Peng, Hoifung Poon, Chris Quirk, Kristina Toutanova, and Wen-tau Yih. 2017. Cross-Sentence N-ary Relation Extraction with Graph LSTMs. Transactions of the Association for Computational Linguistics, 5:101–115.