By Gregory Grefenstette (auth.), Gregory Grefenstette (eds.)
Most of the papers during this quantity have been first offered on the Workshop on Cross-Linguistic details Retrieval that was once held August 22, 1996 dur ing the SIGIR'96 convention. Alan Smeaton of Dublin collage and Paraic Sheridan of the ETH, Zurich, have been the 2 different participants of the medical Committee for this workshop. SIGIR is the organization for Computing Ma chinery (ACM) certain curiosity staff on details Retrieval, and so they have held meetings every year due to the fact 1977. 3 extra papers were extra: bankruptcy four dispensed Cross-Lingual info retrieval describes the EMIR retrieval procedure, one of many first basic cross-language structures to be applied and evaluated; bankruptcy 6 Mapping Vocabularies utilizing Latent Semantic Indexing, which initially seemed as a technical file within the Lab oratory for Computational Linguistics at Carnegie Mellon college in 1991, is incorporated right here since it used to be one of many earliest, even though hard-to-find, publi cations exhibiting the applying of Latent Semantic Indexing to the matter of cross-language retrieval; and bankruptcy 10 A Weighted Boolean version for go Language textual content Retrieval describes a contemporary method of fixing the interpretation time period weighting challenge, particular to Cross-Language details Retrieval. Gregory Grefenstette members Lisa Ballesteros David Hull W, Bruce Croft Gregory Grefenstette middle for clever Xerox learn Centre Europe details Retrieval Grenoble Laboratory machine technology division collage of Massachusetts Thomas ok. Landauer division of Psychology Mark W. Davis and Institute of Cognitive technology Computing study Lab college of Colorado, Boulder New Mexico kingdom college Michael L. Littman Bonnie J.
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Extra info for Cross-Language Information Retrieval
Training data for the pre-translation feedback experiments was the ISM collection described above and the 301 MB San Jose Mercury News (SJMN) database from the Tipster collection. Each English query has a set of relevance judgments or English documents which were pre-judged to be relevant to the query. In order to use these judgments, we needed to test the effectiveness of MRD translations to English. To do this, we created base queries by manually translating the English queries to 28 CHAPTER 3 Spanish (herein referred to as ES-BASE).
It then modifies queries by randomly adding or deleting query terms. Optimization is done by evaluating query fitness after each round of mutations, and selecting the "most fit" to continue to the next generation. Word-by-word translation was chosen as a baseline. The Evolutionary programming approach was the most effective, but results were disappointing, with each of the methods performing well below the baseline. More recently, Sheridan and Ballerini [SB96] utilized co-occurrence thesauri generated from a comparable corpus.
One means for doing so is to create a bi-lingual phrase dictionary from MRD information. We are also exploring ways in which INFINDER [JC94] and local context analysis can be used to reduce phrasal ambiguity. Acknowledgements An early version of this chapter appeared in Proceedings of the 7th International DEXA Conference on Database and Expert Systems Applications (199p). This research is supported by a NASA GSRP grant, #NGT-70358 and is based on work supported by the National Science Foundation, Library of Congress, and Department of Commerce under cooperative agreement number EEC-9209623.
Cross-Language Information Retrieval by Gregory Grefenstette (auth.), Gregory Grefenstette (eds.)