A query term re-weighting approach using document similarity |
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| Institution: | 1. Database Research Group, Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, Iran\n;2. University of Wollongong, Dubai;1. Université de Toulouse, Laboratoire de Génie de Production (LGP), EA 1905, ENIT-INPT, 47 Avenue d’Azereix, BP 1629, Tarbes Cedex 65016, France;2. Université de Toulouse, Faculté de droit, 2 rue du Doyen Gabriel Marty, Toulouse cedex 9 31042, France;1. Polytechnic Institute of Tomar, Tomar, Portugal;2. LIAAD/INESC TEC – INESC Technology and Science, Portugal\n;3. DCC – FCUP, University of Porto, Portugal;4. HULTECH/GREYC, University of Caen Basse-Normandie, Caen, France;5. Department of Mathematics, University of Beira Interior, Covilhã, Portugal;6. Center of Mathematics, University of Beira Interior, Covilhã, Portugal;1. Universidad Autónoma de Madrid, Ciudad Universitaria de Cantoblanco. C/Iván Pavlov, s/n., 28049 Madrid, Spain\n;2. Universidad Nacional de Educación a Distancia, Juan del Rosal, nº 10. 28023, Spain;3. Semantia Lab, Bravo Murillo, 38. 28015, Madrid, Spain |
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| Abstract: | Pseudo-relevance feedback is the basis of a category of automatic query modification techniques. Pseudo-relevance feedback methods assume the initial retrieved set of documents to be relevant. Then they use these documents to extract more relevant terms for the query or just re-weigh the user's original query. In this paper, we propose a straightforward, yet effective use of pseudo-relevance feedback method in detecting more informative query terms and re-weighting them. The query-by-query analysis of our results indicates that our method is capable of identifying the most important keywords even in short queries. Our main idea is that some of the top documents may contain a closer context to the user's information need than the others. Therefore, re-examining the similarity of those top documents and weighting this set based on their context could help in identifying and re-weighting informative query terms. Our experimental results in standard English and Persian test collections show that our method improves retrieval performance, in terms of MAP criterion, up to 7% over traditional query term re-weighting methods. |
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