Abstract
Indexing documents and queries using concepts, instead of word-based indexing, is an alternative approach, and it supposes to give a more meaningful indexing. However, this way of indexing needs to revisit some hypotheses of classical Information Retrieval. Therefore, we propose a new concept weighting approach, namely Relative Weight, which weights concepts with respect to their corresponding text in the documents or queries. In other words, it assigns to each concept a relative weight with respect to the other concepts in the same context. We explore interesting experimental results of our new weighting approach, compared to the classical approaches, through studying the retrieval performance of some classical IR models.
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References
Abdulahhad, K., Chevallet, J.-P., Berrut, C.: Solving Concept mismatch through Bayesian Framework by Extending UMLS Meta-Thesaurus. In: CORIA 2011, Avignon, France, pp. 311–326 (March 2011)
Aronson, A.R.: Metamap: Mapping text to the umls metathesaurus (2006)
Baziz, M.: Indexation conceptuelle guidée par ontologie pour la recherche d’information. Thèse de doctorat, Université Paul Sabatier, Toulouse, France (Décembre 2005)
Bendersky, M., Metzler, D., Bruce Croft, W.: Parameterized concept weighting in verbose queries. In: SIGIR 2011, Beijing, China, pp. 605–614 (2011)
Chevallet, J.-P., Lim, J.-H., Le., D.T.H.: Domain knowledge conceptual inter-media indexing: application to multilingual multimedia medical reports. In: CIKM 2007, Lisbon, Portugal, pp. 495–504 (2007)
Codocedo, V., Lykourentzou, I., Napoli, A.: Semantic Indexing and Retrieval based on Formal Concept Analysis. Technical report (June 2012)
Crestani, F.: Exploiting the similarity of non-matching terms at retrieval time. Inf. Retr. 2(1), 27–47 (2000)
Dozier, C., Kondadadi, R., Al-Kofahi, K., Chaudhary, M., Guo, X.S.: Fast tagging of medical terms in legal text. In: ICAIL, pp. 253–260 (2007)
Fang, H., Tao, T., Zhai, C.: A formal study of information retrieval heuristics. In: SIGIR 2004, Sheffield, United Kingdom, pp. 49–56 (2004)
Le., T.H.D.: Utilisation de ressources externes dans un modèle Bayésien de Recherche d’Information. Application à la recherche d’information multilingue avec UMLS. These, Université Joseph-Fourier - Grenoble I (May 2009)
Luhn, H.P.: The automatic creation of literature abstracts. IBM J. Res. Dev. 2(2), 159–165 (1958)
Maisonnasse, L.: Les supports de vocabulaires pour les systèmes de recherche d’information orientés précision: application aux graphes pour la recherche d’information médicale. These, Université Joseph-Fourier - Grenoble I (May 2008)
Ren, F., Bracewell, D.B.: Advanced information retrieval. Electron. Notes Theor. Comput. Sci. 225, 303–317 (2009)
Robertson, S.E., Walker, S.: Some simple effective approximations to the 2-poisson model for probabilistic weighted retrieval. In: SIGIR 1994, Dublin, Ireland, pp. 232–241 (1994)
Sanderson, M.: Word sense disambiguation and information retrieval. In: SIGIR 1994, Dublin, Ireland, pp. 142–151 (1994)
Singhal, A., Buckley, C., Mitra, M.: Pivoted document length normalization. In: SIGIR 1996, Zurich, Switzerland, pp. 21–29 (1996)
Smucker, M.D., Allan, J., Carterette, B.: A comparison of statistical significance tests for information retrieval evaluation. In: CIKM 2007, Lisbon, Portugal, pp. 623–632 (2007)
Zhai, C., Lafferty, J.: A study of smoothing methods for language models applied to ad hoc information retrieval. In: SIGIR 2001, New Orleans, Louisiana, United States, pp. 334–342 (2001)
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Abdulahhad, K., Chevallet, JP., Berrut, C. (2013). Revisiting the Term Frequency in Concept-Based IR Models. In: Decker, H., Lhotská, L., Link, S., Basl, J., Tjoa, A.M. (eds) Database and Expert Systems Applications. DEXA 2013. Lecture Notes in Computer Science, vol 8055. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40285-2_8
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DOI: https://doi.org/10.1007/978-3-642-40285-2_8
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