On-Demand Associative Cross-Language Information Retrieval

Authors:
André Pinto Geraldo;Viviane P. Moreira;Marcos A. Gonçalves
Affiliations:
Instituto de Informática, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil;Instituto de Informática, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil;Dep. de Ciência da Computação, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil
Venue:
SPIRE '09 Proceedings of the 16th International Symposium on String Processing and Information Retrieval
Year:
2009

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Abstract

This paper proposes the use of algorithms for mining association rules as an approach for Cross-Language Information Retrieval. These algorithms have been widely used to analyse market basket data. The idea is to map the problem of finding associations between sales items to the problem of finding term translations over a parallel corpus. The proposal was validated by means of experiments using queries in two distinct languages: Portuguese and Finnish to retrieve documents in English. The results show that the performance of our proposed approach is comparable to the performance of the monolingual baseline and to query translation via machine translation, even though these systems employ more complex Natural Language Processing techniques. The combination between machine translation and our approach yielded the best results, even outperforming the monolingual baseline.