Iterative translation disambiguation for cross-language information retrieval

Finding a proper distribution of translation probabilities is one of the most important factors impacting the effectiveness of a crosslanguage information retrieval system. In this paper we present a new approach that computes translation probabilities for a given query by using only a bilingual dictionary and a monolingual corpus in the target language. The algorithm combines term association measures with an iterative machine learning approach based on expectation maximization. Our approach considers only pairs of translation candidates and is therefore less sensitive to datasparseness issues than approaches using higher n-grams. The learned translation probabilities are used as query term weights and integrated into a vector-space retrieval system. Results for EnglishGerman cross-lingual retrieval show substantial improvements over a baseline using dictionary lookup without term weighting. Categories and Subject Descriptors H.3 [Information Storage and Retrieval]: Information Sear...

• Type: Conference
• Year: 2005
• Where: SIGIR
• Authors: Christof Monz, Bonnie J. Dorr
• Keywords: Retrieval System, SIGIR 2005, Term Weighting, Translation Probabilities