This paper describes a query classification system for a specialized domain. We take as a case study queries asked to a search engine of an art, cultural and history library and classify them against the library cataloguing categories. We show how click-through links, i.e., the links that a user clicks after submitting a query, can be exploited for extracting information useful to enrich the query as well as for creating the training set for a machine learning based classifier. Moreover, we show how Topic Model can be exploited to further enrich the query with hidden topics induced from the library meta-data. The experimental evaluations show that this system considerably outperforms a matching and ranking classification approach, where queries (and categories) were also enriched with similar information.
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Titolo: | Query Classification Using Topic Models and Support Vector Machine |
Autori: | D. T., Le; Bernardi, Raffaella |
Autori Unitn: | |
Titolo del volume contenente il saggio: | Proceedings of ACL 2012 Student Research Workshop |
Luogo di edizione: | Stroudsburg, PA, USA |
Casa editrice: | ACL - Association for Computational Linguistic |
Anno di pubblicazione: | 2012 |
Handle: | http://hdl.handle.net/11572/101768 |
Appare nelle tipologie: | 04.1 Saggio in atti di convegno (Paper in proceedings) |