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An information-theoretic, vector-space-model approach to cross-language information retrieval*

Published online by Cambridge University Press:  05 January 2011

PETER A. CHEW ,
BRETT W. BADER ,
STEPHEN HELMREICH ,
AHMED ABDELALI  and
STEPHEN J. VERZI
PETER A. CHEW
Affiliation:
Moss Adams LLP, Albuquerque, NM 87110-4189, USA e-mail: PeterAChew@aol.com
BRETT W. BADER
Affiliation:
Sandia National Laboratories, Albuquerque, NM 87185-0519, USA emails: bwbader@sandia.gov, sjverzi@sandia.gov
STEPHEN HELMREICH
Affiliation:
New Mexico State University, New Mexico, 88003-8001, USA emails: helmreich@zianet.com, aabdelal@nmsu.edu
AHMED ABDELALI
Affiliation:
New Mexico State University, New Mexico, 88003-8001, USA emails: helmreich@zianet.com, aabdelal@nmsu.edu
STEPHEN J. VERZI
Affiliation:
Sandia National Laboratories, Albuquerque, NM 87185-0519, USA emails: bwbader@sandia.gov, sjverzi@sandia.gov
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Abstract

In this article, we demonstrate several novel ways in which insights from information theory (IT) and computational linguistics (CL) can be woven into a vector-space-model (VSM) approach to information retrieval (IR). Our proposals focus, essentially, on three areas: pre-processing (morphological analysis), term weighting, and alternative geometrical models to the widely used term-by-document matrix. The latter include (1) PARAFAC2 decomposition of a term-by-document-by-language tensor, and (2) eigenvalue decomposition of a term-by-term matrix (inspired by Statistical Machine Translation). We evaluate all proposals, comparing them to a ‘standard’ approach based on Latent Semantic Analysis, on a multilingual document clustering task. The evidence suggests that proper consideration of IT within IR is indeed called for: in all cases, our best results are achieved using the information-theoretic variations upon the standard approach. Furthermore, we show that different information-theoretic options can be combined for still better results. A key function of language is to encode and convey information, and contributions of IT to the field of CL can be traced back a number of decades. We think that our proposals help bring IR and CL more into line with one another. In our conclusion, we suggest that the fact that our proposals yield empirical improvements is not coincidental given that they increase the theoretical transparency of VSM approaches to IR; on the contrary, they help shed light on why aspects of these approaches work as they do.

Type
Papers
Information
Natural Language Engineering , Volume 17 , Issue 1 , January 2011 , pp. 37 - 70
Copyright
Copyright © Cambridge University Press 2011

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