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• W2891570996 abstract "It is common that entity mentions can contain other mentions recursively. This paper introduces a scalable transition-based method to model the nested structure of mentions. We first map a sentence with nested mentions to a designated forest where each mention corresponds to a constituent of the forest. Our shift-reduce based system then learns to construct the forest structure in a bottom-up manner through an action sequence whose maximal length is guaranteed to be three times of the sentence length. Based on Stack-LSTM which is employed to efficiently and effectively represent the states of the system in a continuous space, our system is further incorporated with a character-based component to capture letter-level patterns. Our model gets the state-of-the-art performances in ACE datasets, showing its effectiveness in detecting nested mentions." @default.
• W2891570996 created "2018-09-27" @default.
• W2891570996 creator A5042749062 @default.
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• W2891570996 date "2018-01-01" @default.
• W2891570996 modified "2023-10-18" @default.
• W2891570996 title "A Neural Transition-based Model for Nested Mention Recognition" @default.
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• W2891570996 doi "https://doi.org/10.18653/v1/d18-1124" @default.
• W2891570996 hasPublicationYear "2018" @default.
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