Information Retrieval

, Volume 17, Issue 1, pp 21–51

Latent word context model for information retrieval

  • Bernard Brosseau-Villeneuve
  • Jian-Yun Nie
  • Noriko Kando
Article

DOI: 10.1007/s10791-013-9220-9

Cite this article as:
Brosseau-Villeneuve, B., Nie, JY. & Kando, N. Inf Retrieval (2014) 17: 21. doi:10.1007/s10791-013-9220-9

Abstract

The application of word sense disambiguation (WSD) techniques to information retrieval (IR) has yet to provide convincing retrieval results. Major obstacles to effective WSD in IR include coverage and granularity problems of word sense inventories, sparsity of document context, and limited information provided by short queries. In this paper, to alleviate these issues, we propose the construction of latent context models for terms using latent Dirichlet allocation. We propose building one latent context per word, using a well principled representation of local context based on word features. In particular, context words are weighted using a decaying function according to their distance to the target word, which is learnt from data in an unsupervised manner. The resulting latent features are used to discriminate word contexts, so as to constrict query’s semantic scope. Consistent and substantial improvements, including on difficult queries, are observed on TREC test collections, and the techniques combines well with blind relevance feedback. Compared to traditional topic modeling, WSD and positional indexing techniques, the proposed retrieval model is more effective and scales well on large-scale collections.

Keywords

Retrieval modelsWord context discrimination (WCD)Word contextTopic modelsWord sense disambiguation (WSD)

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Bernard Brosseau-Villeneuve
    • 1
  • Jian-Yun Nie
    • 1
  • Noriko Kando
    • 2
  1. 1.University of MontréalMontrealCanada
  2. 2.National Institute of InformaticsTokyoJapan