Experiments with dictionary-based CLIR using graded relevance assessments: Improving effectiveness by pseudo-relevance feedback
Research on cross-language information retrieval (CLIR) has typically been restricted to settings using binary relevance assessments. In this paper, we present evaluation results for dictionary-based CLIR using graded relevance assessments in a best match retrieval environment. A text database containing newspaper articles and a related set of 35 search topics were used in the tests. First, monolingual baseline queries were automatically formed from the topics. Secondly, source language topics (in English, German, and Swedish) were automatically translated into the target language (Finnish), using structured target queries. The effectiveness of the translated queries was compared to that of the monolingual queries. Thirdly, pseudo-relevance feedback was used to expand the original target queries. CLIR performance was evaluated using three relevance thresholds: stringent, regular, and liberal. When regular or liberal threshold was used, a reasonable performance was achieved. Using stringent threshold, equally high performance could not be achieved. On all the relevance thresholds the performance of the translated queries was successfully raised by pseudo-relevance feedback based query expansion. However, the performance of the stringent threshold in relation to the other thresholds could not be raised by this method.
graded relevance assessments cross-language information retrieval relevance feedback