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Modeling Sentiment Association in Discourse for Humor Recognition

机译:幽默识别中话语中的模型情绪关联

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Humor is one of the most attractive parts in human communication. However, automatically recognizing humor in text is challenging due to the complex characteristics of humor. This paper proposes to model sentiment association between discourse units to indicate how the punchline breaks the expectation of the setup. We found that discourse relation, sentiment conflict and sentiment transition are effective indicators for humor recognition. On the perspective of using sentiment related features, sentiment association in discourse is more useful than counting the number of emotional words.
机译:幽默是人类交流中最具吸引力的部分之一。然而,由于幽默的复杂特征,自动识别文本中的幽默是挑战。本文建议在话语单位之间模拟情绪关联,以指示策略线如何破坏设置的期望。我们发现话语关系,情感冲突和情感过渡是幽默识别的有效指标。在使用相关特征的角度来看,话语中的情感关联比计算情绪词汇更有用。

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