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- W2893953407 abstract "Textual emotion analysis is an important research issue in natural language processing. In this paper, we address a novel task on emotion, called emotion effect detection, which aims to identify the effect event of a particular emotion happening. To tackle this task, we propose a two-stage model which consists of two components: the identification module and the extraction module. In detail, the identification module aims to judge whether a sentence group contains emotion effect, and the extraction module aims to extract the emotion effect from a sentence group. These two modules are learned with maximum entropy and conditional random field (CRF) methods respectively. Empirical studies demonstrate that the proposed two-stage model yields a better result than the one-stage model." @default.
- W2893953407 created "2018-10-05" @default.
- W2893953407 creator A5028927190 @default.
- W2893953407 date "2018-01-01" @default.
- W2893953407 modified "2023-10-06" @default.
- W2893953407 title "Emotion Effect Detection with a Two-Stage Model" @default.
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- W2893953407 doi "https://doi.org/10.1007/978-3-030-00021-9_46" @default.
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