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Academic Paper


Title: An information-theoretic, vector-space-model approach to cross-language information retrieval
Author: Peter A. Chew
Email: click here TO access email
Homepage: http://www.dissertation.com/library/1121784a.htm
Institution: Sandia National Laboratories
Author: Brett W. Bader
Institution: Sandia National Laboratories
Author: Stephen Helmreich
Institution: New Mexico State University
Author: Ahmed Abdelali
Institution: New Mexico State University
Author: Stephen J. Verzi
Institution: Sandia National Laboratories
Linguistic Field: Computational Linguistics
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.

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This article appears IN Natural Language Engineering Vol. 17, Issue 1, which you can READ on Cambridge's site or on LINGUIST .



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