Part of Speech Based Term Weighting for Information Retrieval

  • Christina Lioma
  • Roi Blanco
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5478)


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.


Information Retrieval Machine Translation Retrieval Model Retrieval Performance Mean Average Precision 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Christina Lioma
    • 1
  • Roi Blanco
    • 2
  1. 1.Computer ScienceKatholieke Universiteit LeuvenBelgium
  2. 2.Computer ScienceLa Coruna UniversitySpain

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