Language barrier is the major problem that people face in searching for, retrieving, and understanding multilingual collections on the Internet. This paper deals with query translation and document translation in a Chinese-English information retrieval system called MTIR. Bilingual dictionary and monolingual corpus-based approaches are adopted to select suitable translated query terms. A machine transliteration algorithm is introduced to resolve proper name searching. We consider several design issues for document translation, including which material is translated, what roles the HTML tags play in translation, what the tradeoff is between the speed performance and the translation performance, and what form the translated result is presented in. About 100,000 Web pages translated in the last four months of 1997 are used for quantitative study of online and real-time Web page translation.

This paper deals with translation ambiguity and target polysemy problems together.Two monolingual balanced corpora are employed to learn word co-occurrence for translation ambiguity resolution, and augmented translation restrictions for target polysemy resolution.Experiments show that the model achieves 62.92% of monolingual information retrieval, and is 40.80% addition to the select-all model.Combining the target polysemy resolution, the retrieval performance is about 10.11% increase to the model resolving translation ambiguity only.