Part of Speech Based Term Weighting for Information Retrieval
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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.
Originalsprog | Engelsk |
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Titel | ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval |
Publikationsdato | 2009 |
Sider | 412-423 |
Status | Udgivet - 2009 |
Eksternt udgivet | Ja |
ID: 38252017