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• W3005275511 abstract "As a basic task of natural language processing, the performance of biomedical event trigger identification is lower than that of named entity recognition(NER), which is mainly limited by complex events and the scale of annotated corpus. In recent years, researches on the identification of biomedical event trigger mainly focus on the small size of corpus, and tend to use external knowledge or joint training to improve the final performance. However, there are few researches on the insufficient mining of the inherent features of texts caused by complex events. This article focuses on biomedical event trigger identification in text articles using a neural network model that combines bidirectional gated Recurrent Unit with a self-attention mechanism. In this method, context-related information is captured by bidirectional gated recurrent unit to better understand and express sentence structure, self-attention mechanism is adopted to fully explore the features of text itself, and finally to improve the performance of trigger identification of complex events. A series of comparative experiments were carried out on MLEE corpus, and the experimental results verified the effectiveness of the method." @default.
• W3005275511 created "2020-02-14" @default.
• W3005275511 creator A5018424554 @default.
• W3005275511 creator A5068048648 @default.
• W3005275511 date "2019-11-01" @default.
• W3005275511 modified "2023-09-24" @default.
• W3005275511 title "Combined Self-attention Mechanism For Biomedical Event Trigger Identification" @default.
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• W3005275511 doi "https://doi.org/10.1109/bibm47256.2019.8983274" @default.
• W3005275511 hasPublicationYear "2019" @default.
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