Noun phrase translations for Cross-Language Document Selection

Abstract

This paper presents results for the CLEF interactive Cross-Language Document Selection task at the UNED. Two translations techniques were compared: the standard Systran translations provided by CLEF organizers as baseline, and a phrase-based pseudo-translation approach that uses a phrase alignment algorithm based on comparable corpora. The hypothesis being tested was that noun phrase translations could serve as summarized information for relevance judgment without compromising the precision of such judgments. In addition, we wanted to have an indirect measure of the quality of our phrase extraction process, that had been previously developed for an interactive CLIR application. The results of the experiment confirm that the hypothesis is reasonable: a set of 8 monolingual Spanish speakers judged English documents with the same precision for both systems, but achieved 52% more recall using phrasal translations than using full Systran translations.