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Construction of query concepts based on feature clustering of documents

机译：基于文档特征聚类的查询概念构建

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摘要

In Information Retrieval, since it is hard to identify users' information needs, many approaches have been tried to solve this problem by expanding initial queries and reweighting the terms in the expanded queries using users' relevance judgments. Although relevance feedback is most effective when relevance information about retrieved documents is provided by users, it is not always available. Another solution is to use correlated terms for query expansion. The main problem with this approach is how to construct the term-term correlations that can be used effectively to improve retrieval performance. In this study, we try to construct query concepts that denote users' information needs from a document space, rather than to reformulate initial queries using the term correlations and/or users' relevance feedback. To form query concepts, we extract features from each document, and then cluster the features into primitive concepts that are then used to form query concepts. Experiments are performed on the Associated Press (AP) dataset taken from the TREC collection. The experimental evaluation shows that our proposed framework called QCM (Query Concept Method) outperforms baseline probabilistic retrieval model on TREC retrieval.
机译：在信息检索中，由于难以识别用户的信息需求，因此尝试了许多方法来解决此问题，方法是扩展初始查询并使用用户的相关性判断对扩展查询中的术语进行加权。尽管当用户提供有关检索到的文档的相关性信息时，相关性反馈是最有效的，但并非总是可用。另一种解决方案是使用关联词进行查询扩展。这种方法的主要问题是如何构建可有效用于提高检索性能的项-项相关性。在这项研究中，我们尝试构建表示文档空间中用户信息需求的查询概念，而不是使用术语相关性和/或用户相关性反馈来重新构造初始查询。为了形成查询概念，我们从每个文档中提取特征，然后将这些特征聚集到原始概念中，然后将这些原始概念用于形成查询概念。实验是从TREC馆藏的美联社（AP）数据集中进行的。实验评估表明，我们提出的称为QCM（查询概念方法）的框架在TREC检索方面优于基线概率检索模型。

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