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Phrase Pair Classification for Identifying Subtopics

机译:短语对分类用于识别子主题

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摘要

Automatic identification of subtopics for a given topic is desirable because it eliminates the need for manual construction of domain-specific topic hierarchies. In this paper, we design features based on corpus statistics to design a classifier for identifying the (subtopic, topic) links between phrase pairs. We combine these features along with the commonly-used syntactic patterns to classify phrase pairs from datasets in Computer Science and WordNet. In addition, we show a novel application of our is-a-subtopic-of classifier for query expansion in Expert Search and compare it with pseudo-relevance feedback.
机译:对于给定主题的子主题的自动标识是理想的,因为它消除了手动构建特定于域的主题层次结构的需要。在本文中,我们基于语料库统计信息设计特征,以设计用于识别短语对之间的(子主题,主题)链接的分类器。我们将这些功能与常用的句法模式结合起来,以对来自Computer Science和WordNet中数据集的短语对进行分类。此外,我们展示了我们的is-a-subtopic-分类器在专家搜索中用于查询扩展的新颖应用,并将其与伪相关反馈进行了比较。

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