Syntactic Patterns Improve Information Extraction for Medical Search

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

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 neural and linear) for information extraction of these medically relevant categories. We present an analysis of the type of patterns exploited and of 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.
Anthology ID:
N18-2060
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Editors:
Marilyn Walker, Heng Ji, Amanda Stent
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
371–377
Language:
URL:
https://aclanthology.org/N18-2060
DOI:
10.18653/v1/N18-2060
Bibkey:
Cite (ACL):
Roma Patel, Yinfei Yang, Iain Marshall, Ani Nenkova, and Byron Wallace. 2018. Syntactic Patterns Improve Information Extraction for Medical Search. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 371–377, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
Syntactic Patterns Improve Information Extraction for Medical Search (Patel et al., NAACL 2018)
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PDF:
https://preview.aclanthology.org/nschneid-patch-3/N18-2060.pdf
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