@inbook{8dce2a459a16422bb39255b461292f16,
title = "Syntactic patterns improve information extraction for medical search",
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.",
author = "Roma Patel and Yinfei Yang and Iain Marshall and Ani Nenkova and Wallace, {Byron C.}",
year = "2018",
month = jan,
day = "1",
language = "English",
series = "NAACL HLT 2018 - 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference",
publisher = "Association for Computational Linguistics (ACL)",
pages = "371--377",
booktitle = "Short Papers",
note = "2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018 ; Conference date: 01-06-2018 Through 06-06-2018",
}