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. 2018 Jun;2018(Short Paper):371-377.

Syntactic Patterns Improve Information Extraction for Medical Search

Roma Patel  1   2 , Yinfei Yang  1   2   3   4 , Iain Marshall  3 , Ani Nenkova  1 , Byron C Wallace  4
Affiliations

Syntactic Patterns Improve Information Extraction for Medical Search

Roma Patel et al. Proc Conf. 2018 Jun.

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.

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Figures

Figure 1:
Figure 1:
Scatter of PCA-reduced embeddings clustered using K-means. <> brackets show the syntactic pattern n-grams given by Autoslog-TS that are embedding in the same space as unigrams.
See this image and copyright information in PMC

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