{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T05:38:59Z","timestamp":1777354739185,"version":"3.51.4"},"reference-count":2,"publisher":"MIT Press - Journals","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["TACL"],"published-print":{"date-parts":[[2017,12]]},"abstract":"<jats:p> 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. <\/jats:p>","DOI":"10.1162\/tacl_a_00049","type":"journal-article","created":{"date-parts":[[2018,12,28]],"date-time":"2018-12-28T15:42:50Z","timestamp":1546011770000},"page":"101-115","source":"Crossref","is-referenced-by-count":284,"title":["Cross-Sentence <i>N<\/i>-ary Relation                     Extraction with Graph LSTMs"],"prefix":"10.1162","volume":"5","author":[{"given":"Nanyun","family":"Peng","sequence":"first","affiliation":[{"name":"Center for Language and Speech Processing, Computer Science Department,                         Johns Hopkins University, Baltimore, MD, USA,"}]},{"given":"Hoifung","family":"Poon","sequence":"additional","affiliation":[{"name":"Microsoft Research, Redmond, WA, USA,"}]},{"given":"Chris","family":"Quirk","sequence":"additional","affiliation":[{"name":"Microsoft Research, Redmond, WA, USA,"}]},{"given":"Kristina","family":"Toutanova","sequence":"additional","affiliation":[{"name":"Google Research, Seattle, WA, USA,"}]},{"given":"Wen-tau","family":"Yih","sequence":"additional","affiliation":[{"name":"Microsoft Research, Redmond, WA, USA,"}]}],"member":"281","reference":[{"key":"p_5","volume":"200","author":"Bunescu Razvan C","journal-title":"Raymond J Mooney."},{"key":"p_56","first-page":"LL","volume":"201","author":"Xue Nianwen","journal-title":"Attapol Rutherford."}],"container-title":["Transactions of the Association for Computational Linguistics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mitpressjournals.org\/doi\/pdf\/10.1162\/tacl_a_00049","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,12]],"date-time":"2021-03-12T21:38:06Z","timestamp":1615585086000},"score":1,"resource":{"primary":{"URL":"https:\/\/direct.mit.edu\/tacl\/article\/43389"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,12]]},"references-count":2,"alternative-id":["10.1162\/tacl_a_00049"],"URL":"https:\/\/doi.org\/10.1162\/tacl_a_00049","relation":{},"ISSN":["2307-387X"],"issn-type":[{"value":"2307-387X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,12]]}}}