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Exploiting Discourse Relations between Sentences for Text Clustering

机译:利用文本聚类句子的话语关系

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Over the years, the usage of discourse relations has been proven to enhance many applications such as text summarization, question answering and natural language generation. This paper proposes an approach that expands the benefit of discourse relations for natural language processing from a different aspect. We exploit the discourse relations existing between sentences to generate clusters of similar sentences from document sets. We first examined and defined the type of discourse relations that useful to retrieve sentences with identical content. We then assigned these relations to each sentence pair using a machine learning method. Finally we performed discourse relation-based clustering algorithm to generate clusters of similar sentences. We evaluated our method by measuring the cohesion and separation of the clusters and compared to a well recognized clustering method. The experimental result shows that our method performed significantly well, which demonstrated that discourse relation between sentences can be exploited for text clustering.
机译:多年来,已证明话语关系的使用是为了提高许多诸如文本摘要,问题应答和自然语言生成等申请。本文提出了一种拓展了不同方面的自然语言处理的话语关系的益处的方法。我们利用句子之间存在的话语关系来生成文档集的类似句子的集群。我们首先检查并定义了用于检索具有相同内容的句子的话语关系类型。然后,我们使用机器学习方法将这些关系分配给每个句子对。最后,我们执行了基于话语关系的聚类算法来生成类似句子的集群。我们通过测量簇的凝聚力和分离并与良好认可的聚类方法进行评估。实验结果表明,我们的方法效果显着良好,这表明可以利用句子之间的话语关系进行文本聚类。

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