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- W76425264 abstract "Negation and speculation are common in natural language text. Many applications, such as biomedical text mining and clinical information extraction, seek to distinguish positive/factual objects from negative/speculative ones (i.e., to determine what is negated or speculated) in biomedical texts. This paper proposes a novel task, called negation and speculation target identification, to identify the target of a negative or speculative expression. For this purpose, a new layer of the target information is incorporated over the BioScope corpus and a machine learning algorithm is proposed to automatically identify this new information. Evaluation justifies the effectiveness of our proposed approach on negation and speculation target identification in biomedical texts." @default.
- W76425264 created "2016-06-24" @default.
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- W76425264 date "2014-01-01" @default.
- W76425264 modified "2023-09-27" @default.
- W76425264 title "Negation and Speculation Target Identification" @default.
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- W76425264 doi "https://doi.org/10.1007/978-3-662-45924-9_4" @default.
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