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Query expansion using named entity disambiguation for a question-answering system

机译:使用命名实体消歧词对问答系统进行查询扩展

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Due to the exponential growth in the size of available data, people often prefer direct answers in many cases, instead of a set of ranked documents while searching using a search engine. Furthermore, people often do not provide enough information regarding their search. Query expansion would help in such cases so that queries would be best understood by search engines. Although the queries are expanded, many a times, they end up with wrong results due to the entity mentions in the questions. Hence, we need to disambiguate named entities properly so as to choose the right mention. In this paper, a method is proposed for named entity disambiguation in order to help in query expansion. An adapted Lesk similarity measure is used to find the similarity between questions and the target Wikipedia disambiguation pages. The proposed approach has shown very good results in disambiguating organization- and miscellaneous-type entity mentions.
机译:由于可用数据的大小呈指数增长,因此在许多情况下,人们在使用搜索引擎进行搜索时,通常更喜欢直接答案,而不是一组排名较高的文档。此外,人们通常没有提供有关其搜索的足够信息。在这种情况下,查询扩展会有所帮助,以便搜索引擎可以更好地理解查询。尽管查询多次被扩展,但由于问题中提到的实体,最终结果还是错误的。因此,我们需要适当地消除命名实体的歧义,以便选择正确的提及。本文提出了一种用于命名实体消歧的方法,以帮助查询扩展。经过调整的Lesk相似性度量用于查找问题与目标Wikipedia歧义消除页面之间的相似性。所提出的方法在消除组织和其他类型的实体提及方面显示出非常好的结果。

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