One of the main hurdles to improved CLIR effectiveness is resolving ambiguity associated with translation. Availability of resources is also a problem. First we present a technique based on co-occurrence statistics from unlinked corpora which can be used to reduce the ambiguity associated with phrasal and term translation. We then combine this method with other techniques for reducing ambiguity and achieve more than 90% monolingual effectiveness. Finally, we compare the co-occurrence method with parallel corpus and machine translation techniques and show that good retrieval effectiveness can be achieved without complex resources.

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Dictionary methods for cross-language information retrieval give performance below that for mono-lingual retrieval. Failure to translate multi-term phrases has km shown to be one of the factors responsible for the errors associated with dictionary methods. First, we study the importance of phrasaI translation for this approach. Second, we explore the role of phrases in query expansion via local context analysis and local feedback and show how they can be used to significantly reduce the error associated with automatic dictionary translation.