Part of Speech Based Term Weighting for Information Retrieval

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LIOMA, Christina, Roi BLANCO, 2009. Part of Speech Based Term Weighting for Information Retrieval. In: BOUGHANEM, Mohand, ed., Catherine BERRUT, ed., Josiane MOTHE, ed., Chantal SOULE-DUPUY, ed.. Advances in Information Retrieval. Berlin:Springer, pp. 412-423. ISBN 978-3-642-00957-0

@inproceedings{Lioma2009Speec-2664, title={Part of Speech Based Term Weighting for Information Retrieval}, year={2009}, doi={10.1007/978-3-642-00958-7_37}, number={5478}, isbn={978-3-642-00957-0}, address={Berlin}, publisher={Springer}, series={Lecture Notes in Computer Science}, booktitle={Advances in Information Retrieval}, pages={412--423}, editor={Boughanem, Mohand and Berrut, Catherine and Mothe, Josiane and Soule-Dupuy, Chantal}, author={Lioma, Christina and Blanco, Roi} }

2011-03-23T09:58:43Z Lioma, Christina 2011-03-23T09:58:43Z Part of Speech Based Term Weighting for Information Retrieval Blanco, Roi deposit-license Lioma, Christina Publ. in: Advances in information retrieval: 31th European Conference on IR Research, ECIR 2009, Toulouse, France, April 6 - 9, 2009; proceedings / Mohand Boughanem ... (eds.). (= LNCS ; 5478) Berlin: Springer, 2009, pp. 412-423 eng Blanco, Roi Automatic language processing tools typically assign to terms so-called weights' corresponding to the contribution of terms to information content. Traditionally, term weights are computed from lexical statistics, e.g., term frequencies. We propose a new type of term weight that is computed from part of speech (POS) n-gram statistics. The proposed POS-based term weight represents how informative a term is in general, based on the POS contexts' in which it generally occurs in language. We suggest five different computations of POS-based term weights by extending existing statistical approximations of term information measures. We apply these POS-based term weights to information retrieval, by integrating them into the model that matches documents to queries. Experiments with two TREC collections and 300 queries, using TF-IDF & BM25 as baselines, show that integrating our POS-based term weights to retrieval always leads to gains (up to +33.7% from the baseline). Additional experiments with a different retrieval model as baseline (Language Model with Dirichlet priors smoothing) and our best performing POS-based term weight, show retrieval gains always and consistently across the whole smoothing range of the baseline. 2009

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