Abstract: A “Cross-Lingual Unified Relevance Model” provides a feedback model that improves a machine-learned ranker for a language with few training resources, using feedback from a more complete ranker for a language that has more training resources. The model focuses on linguistically non-local queries, such as “world cup” (English language/U.S. market) and “copa mundial” (Spanish language/Mexican market), that have similar user intent in different languages and markets or regions, thus allowing the low-resource ranker to receive direct relevance feedback from the high-resource ranker. Among other things, the Cross-Lingual Unified Relevance Model differs from conventional relevancy-based techniques by incorporating both query- and document-level features.

Abstract: A “Cross-Lingual Unified Relevance Model” provides a feedback model that improves a machine-learned ranker for a language with few training resources, using feedback from a more complete ranker for a language that has more training resources. The model focuses on linguistically non-local queries, such as “world cup” (English language/U.S. market) and “copa mundial” (Spanish language/Mexican market), that have similar user intent in different languages and markets or regions, thus allowing the low-resource ranker to receive direct relevance feedback from the high-resource ranker. Among other things, the Cross-Lingual Unified Relevance Model differs from conventional relevancy-based techniques by incorporating both query- and document-level features.

Abstract: A method and system for generating a bilingual dictionary that maps words of the source language to words of a target language is provided. A Cross-Lingual Information Retrieval (“CLIR”) system accesses a parallel collection that is comprised of a parallel source collection and a parallel target collection, and generates a similarity score for sentences of the parallel target collection indicating the similarity of those sentences to sentences of the target collection. When the CLIR system generates a bilingual dictionary from the sentences of the parallel collection, it factors in the similarities of the sentences of the parallel target collection to sentences in the target collection. By factoring these similarities, the CLIR system allows sentences with a high similarity to have a greater influence on the mapping of words of the source language to the words of the target language than sentences with a low similarity.