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How can NLP Tasks Mutually Benefit Sentiment Analysis? A Holistic Approach to Sentiment Analysis

机译:NLP任务如何相互效益情绪分析?一种情绪分析的整体方法

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Existing opinion analysis techniques rely on the clues within the sentence that focus on the sentiment analysis task itself. However, the sentiment analysis task is not isolated from other NLP tasks (co-reference resolution, entity linking, etc) but they can benefit each other. In this paper, we define dependencies between sentiment analysis and other tasks, and express the dependencies in first order logic rules regardless of the representations of different tasks. The conceptual framework proposed in this paper using such dependency rules as constraints aims at exploiting information outside the sentence and outside the document to improve sentiment analysis. Further, the framework allows exception to the rules.
机译:现有的观点分析技术依赖于关注情绪分析任务本身的句子中的线索。但是,情感分析任务不是与其他NLP任务(共同参考分辨率,实体链接等)隔离,但它们可以互相受益。在本文中,我们在情感分析和其他任务之间定义依赖关系,并在一阶逻辑规则中表达依赖关系,而不管不同任务的表示。本文提出的概念框架,使用这种依赖规则作为约束,旨在利用句子之外的信息以及文档外的信息来改善情绪分析。此外,该框架允许例外规则。

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