期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2017
卷号：5
期号：1
页码：568
DOI：10.15680/IJIRCCE.2017.0501114
出版社：S&S Publications
摘要：Web search queries are often ambiguous or multi-faceted, which makes a simple ranked list of resultsinadequate. To assist information finding for such faceted queries, we explore a technique that explicitly representsinteresting facets of a query using groups of semantically related terms extracted from search results. As an example,for the query “baggage allowance”, these groups might be different airlines, different flight types (domestic,international), or different travel classes (first, business, economy). We name these groups query facets and the terms inthese groups facet terms. We develop a supervised approach based on a graphical model to recognize query facets fromthe noisy candidates found. The graphical model learns how likely a candidate term is to be a facet term as well as howlikely two terms are to be grouped together in a query facet, and captures the dependencies between the two factors. Wepropose two algorithms for approximate inference on the graphical model since exact inference is intractable. Ourevaluation combines recall and precision of the facet terms with the grouping quality. Experimental results on a sampleof web queries show that the supervised method significantly outperforms existing approaches, which are mostlyunsupervised, suggesting that query facet extraction can be effectively learned.
