Cross-language information retrieval using PARAFAC2

Peter A. Chew, Brett W. Bader, Tamara G. Kolda, Ahmed Abdelali

Research output: Chapter in Book/Report/Conference proceedingConference contribution

53 Citations (Scopus)

Abstract

A standard approach to cross-language information retrieval (CLIR) uses Latent Semantic Analysis (LSA) in conjunction with a multilingual parallel aligned corpus. This approach has been shown to be successful in identifying similar documents across languages - or more precisely, retrieving the most similar document in one language to a query in another language. However, the approach has severe drawbacks when applied to a related task, that of clustering documents 'language independently', so that documents about similar topics end up closest to one another in the semantic space regardless of their language. The problem is that documents are generally more similar to other documents in the same language than they are to documents in a different language, but on the same topic. As a result, when using multilingual LSA, documents will in practice cluster by language, not by topic. We propose a novel application of PARAFAC2 (which is a variant of PARAFAC, a multi-way generalization of the singular value decomposition [SVD]) to overcome this problem. Instead of forming a single multilingual term-by-document matrix which, under LSA, is subjected to SVD, we form an irregular three-way array, each slice of which is a separate term-by-document matrix for a single language in the parallel corpus. The goal is to compute an SVD for each language such that V (the matrix of right singular vectors) is the same across all languages. Effectively, PARAFAC2 imposes the constraint, not present in standard LSA, that the 'concepts' in all documents in the parallel corpus are the same regardless of language. Intuitively, this constraint makes sense, since the whole purpose of using a parallel corpus is that exactly the same concepts are expressed in the translations. We tested this approach by comparing the performance of PARAFAC2 with standard LSA in solving a particular CLIR problem. From our results, we conclude that PARAFAC2 offers a very promising alternative to LSA not only for multilingual document clustering, but also for solving other problems in crosslanguage information retrieval.

Original languageEnglish
Title of host publicationProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Pages143-152
Number of pages10
DOIs
Publication statusPublished - 14 Dec 2007
Externally publishedYes
EventKDD-2007: 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - San Jose, CA, United States
Duration: 12 Aug 200715 Aug 2007

Other

OtherKDD-2007: 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
CountryUnited States
CitySan Jose, CA
Period12/8/0715/8/07

Fingerprint

Query languages
Semantics
Singular value decomposition
Information retrieval

Keywords

  • Clustering
  • Information retrieval
  • Latent semantic analysis (LSA)
  • Multilingual
  • PARAFAC2

ASJC Scopus subject areas

  • Information Systems

Cite this

Chew, P. A., Bader, B. W., Kolda, T. G., & Abdelali, A. (2007). Cross-language information retrieval using PARAFAC2. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 143-152) https://doi.org/10.1145/1281192.1281211

Cross-language information retrieval using PARAFAC2. / Chew, Peter A.; Bader, Brett W.; Kolda, Tamara G.; Abdelali, Ahmed.

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2007. p. 143-152.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Chew, PA, Bader, BW, Kolda, TG & Abdelali, A 2007, Cross-language information retrieval using PARAFAC2. in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. pp. 143-152, KDD-2007: 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Jose, CA, United States, 12/8/07. https://doi.org/10.1145/1281192.1281211
Chew PA, Bader BW, Kolda TG, Abdelali A. Cross-language information retrieval using PARAFAC2. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2007. p. 143-152 https://doi.org/10.1145/1281192.1281211
Chew, Peter A. ; Bader, Brett W. ; Kolda, Tamara G. ; Abdelali, Ahmed. / Cross-language information retrieval using PARAFAC2. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2007. pp. 143-152
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