@inproceedings{patel-etal-2018-syntactic,
title = "Syntactic Patterns Improve Information Extraction for Medical Search",
author = "Patel, Roma and
Yang, Yinfei and
Marshall, Iain and
Nenkova, Ani and
Wallace, Byron",
editor = "Walker, Marilyn and
Ji, Heng and
Stent, Amanda",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-2060/",
doi = "10.18653/v1/N18-2060",
pages = "371--377",
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."
}
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<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.</abstract>
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%0 Conference Proceedings
%T Syntactic Patterns Improve Information Extraction for Medical Search
%A Patel, Roma
%A Yang, Yinfei
%A Marshall, Iain
%A Nenkova, Ani
%A Wallace, Byron
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F patel-etal-2018-syntactic
%X 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.
%R 10.18653/v1/N18-2060
%U https://aclanthology.org/N18-2060/
%U https://doi.org/10.18653/v1/N18-2060
%P 371-377
Markdown (Informal)
[Syntactic Patterns Improve Information Extraction for Medical Search](https://aclanthology.org/N18-2060/) (Patel et al., NAACL 2018)
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.