Translation Resources, Merging Strategies, and Relevance Feedback for Cross-Language Information Retrieval

This paper describes the official runs of the Twenty-One group for the first CLEF workshop. The Twenty-One group participated in the monolingual, bilingual and multilingual tasks. The following new techniques are introduced in this paper. In the bilingual task we experimented with different methods...

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description This paper describes the official runs of the Twenty-One group for the first CLEF workshop. The Twenty-One group participated in the monolingual, bilingual and multilingual tasks. The following new techniques are introduced in this paper. In the bilingual task we experimented with different methods to estimate translation probabilities. In the multilingual task we experimented with refinements on raw-score merging techniques and with a new relevance feedback algorithm that re-estimates both the model’s translation probabilities and the relevance weights. Finally, we performed preliminary experiments to exploit the web to generate translation probabilities and bilingual dictionaries, notably for English-Italian and English-Dutch.
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identifier ISSN: 0302-9743
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source Springer Books
subjects Applied sciences
Collection Size
Computer science
control theory
systems
Exact sciences and technology
Information systems. Data bases
Memory organisation. Data processing
Parallel Corpus
Query Term
Relevance Feedback
Software
Translation Probability
title Translation Resources, Merging Strategies, and Relevance Feedback for Cross-Language Information Retrieval
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