Computing term translation probabilities with generalized latent semantic analysis

Authors:
Irina Matveeva;Gina-Anne Levow
Affiliations:
University of Chicago, Chicago, IL;University of Chicago, Chicago, IL
Venue:
EACL '06 Proceedings of the Eleventh Conference of the European Chapter of the Association for Computational Linguistics: Posters & Demonstrations
Year:
2006

Citing 7
Cited 1

A language modeling approach to information retrieval

Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Information retrieval as statistical translation

Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Document language models, query models, and risk minimization for information retrieval

Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Document clustering with committees

SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Introduction to Modern Information Retrieval

Introduction to Modern Information Retrieval
Dictionary-based techniques for cross-language information retrieval

Information Processing and Management: an International Journal - Special issue: Cross-language information retrieval
Frequency estimates for statistical word similarity measures

NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1

Document representation and multilevel measures of document similarity

NAACL-DocConsortium '06 Proceedings of the 2006 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume: doctoral consortium

Quantified Score

Hi-index	0.00

Visualization

Abstract

Term translation probabilities proved an effective method of semantic smoothing in the language modelling approach to information retrieval tasks. In this paper, we use Generalized Latent Semantic Analysis to compute semantically motivated term and document vectors. The normalized cosine similarity between the term vectors is used as term translation probability in the language modelling framework. Our experiments demonstrate that GLSA-based term translation probabilities capture semantic relations between terms and improve performance on document classification.