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- W2914372457 abstract "Detecting the scope of speculation and negation inbiomedical texts is an important research topic in biomedical community. Previous work shows that syntax information is crucial for the task, and these work usually models it locally by extracting a set of manually-crafted features such as dependency paths. In this paper, we explore empirically a recursive neural network model, representing whole dependency trees globally and learning automatically syntactic features, to better incorporate dependency-based syntax information. Experiments on BioScope corpus show that our model achieves competitive performance, outperforming previous systems." @default.
- W2914372457 created "2019-02-21" @default.
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- W2914372457 date "2018-12-01" @default.
- W2914372457 modified "2023-09-27" @default.
- W2914372457 title "Detecting the Scope of Negation and Speculation in Biomedical Texts by Using Recursive Neural Network" @default.
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- W2914372457 doi "https://doi.org/10.1109/bibm.2018.8621261" @default.
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