Syntactic patterns improve information extraction for medical search

Roma Patel, Yinfei Yang, Iain Marshall, Ani Nenkova, Byron C. Wallace

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

10 Citations (Scopus)

Abstract

Medical professionals search the published literature by specifying the type of patients, the medical intervention(s) and the outcome measure( s) of interest. In this paper we demonstrate how features encoding syntactic patterns improve the performance of state-of-the-art sequence tagging models (both linear and neural) for information extraction of these medically relevant categories. We present an analysis of the type of patterns exploited, and the semantic space induced for these, i.e., the distributed representations learned for identified multi-token patterns. We show that these learned representations differ substantially from those of the constituent unigrams, suggesting that the patterns capture contextual information that is otherwise lost.

Original languageEnglish
Title of host publicationShort Papers
PublisherAssociation for Computational Linguistics (ACL)
Pages371-377
Number of pages7
ISBN (Electronic)9781948087292
Publication statusPublished - 1 Jan 2018
Event2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018 - New Orleans, United States
Duration: 1 Jun 20186 Jun 2018

Publication series

NameNAACL HLT 2018 - 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference
Volume2

Conference

Conference2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018
Country/TerritoryUnited States
CityNew Orleans
Period1/06/20186/06/2018

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