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Detecting verbose queries and improving information retrieval

机译：检测详细查询并改善信息检索

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摘要

Although most of the queries submitted to search engines are composed of a few keywords and have a length that ranges from three to six words, more than 15% of the total volume of the queries are verbose, introduce ambiguity and cause topic drifts. We consider verbosity a different property of queries from length since a verbose query is not necessarily long, it might be succinct and a short query might be verbose. This paper proposes a methodology to automatically detect verbose queries and conditionally modify queries. The methodology proposed in this paper exploits state-of-the-art classification algorithms, combines concepts from a large linguistic database and uses a topic gisting algorithm we designed for verbose query modification purposes. Our experimental results have been obtained using the TREC Robust track collection, thirty topics classified by difficulty degree, four queries per topic classified by verbosity and length, and human assessment of query verbosity. Our results suggest that the methodology for query modification conditioned to query verbosity detection and topic gisting is significantly effective and that query modification should be refined when topic difficulty and query verbosity are considered since these two properties interact and query verbosity is not straightforwardly related to query length.
机译：尽管提交给搜索引擎的大多数查询都是由几个关键字组成，并且长度在3到6个词之间，但是超过总查询量的15％是冗长的，会引起歧义并导致主题漂移。由于冗长的查询不一定很长，所以冗长是查询的一种不同性质，因为冗长的查询不一定很长，简短的查询可能很冗长。本文提出了一种自动检测详细查询并有条件地修改查询的方法。本文提出的方法利用了最新的分类算法，结合了来自大型语言数据库的概念，并使用了我们为详细查询修改目的而设计的主题选题算法。我们的实验结果是通过使用TREC Robust跟踪集合，按难度度分类的30个主题，按详细程度和长度分类的每个主题4个查询以及对查询详细程度的人工评估获得的。我们的结果表明，以查询详细程度为检测条件和主题主语为条件的查询修改方法非常有效，并且当考虑主题难度和查询详细程度时，应修改查询修改，因为这两个属性相互作用并且查询详细程度与查询长度不直接相关。

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