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Towards automatic generation of query taxonomy: a hierarchical query clustering approach

机译:迈向自动生成查询分类法:分层查询聚类方法

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Most previous work on automatic query clustering generated a flat, un-nested partition of query terms. In this work, we discuss the organization of query terms into a hierarchical structure and construct a query taxonomy in an automatic way. The proposed approach is designed based on a hierarchical agglomerative clustering algorithm to hierarchically group similar queries and generate cluster hierarchies using a novel cluster partition technique. The search processes of real-world search engines are combined to obtain highly ranked Web documents as the feature source for each query term. Preliminary experiments show that the proposed approach is effective for obtaining thesaurus information for query terms, and is also feasible for constructing a query taxonomy which provides a basis for in-depth analysis of users' search interests and domain-specific vocabulary on a larger scale.
机译:以前有关自动查询集群的大多数工作都生成了扁平的,未嵌套的查询字词分区。在这项工作中,我们讨论了将查询词组织成分层结构并以自动方式构造查询分类的方法。所提出的方法是基于分层聚集聚类算法设计的,以使用一种新颖的集群分区技术对相似的查询进行分层分组并生成集群层次结构。结合现实世界中搜索引擎的搜索过程,可以获得排名较高的Web文档,作为每个查询词的特征源。初步实验表明,该方法不仅可以有效地获取查询词的词库信息,而且可以用于构建查询分类法,为进一步深入分析用户的搜索兴趣和特定领域的词汇提供依据。

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