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- W2963354565 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." @default.
- W2963354565 created "2019-07-30" @default.
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- W2963354565 date "2018-01-01" @default.
- W2963354565 modified "2023-09-23" @default.
- W2963354565 title "Syntactic Patterns Improve Information Extraction for Medical Search" @default.
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- W2963354565 doi "https://doi.org/10.18653/v1/n18-2060" @default.
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