Resolving Translation Ambiguity and Target Polysemy in Cross-Language Information Retrieval＋

This paper deals with translation ambiguity and target polysemy problems together. Two monolingual balanced corpora are employed to learn word co-occurrence for the purpose of translation ambiguity resolution and augmented translation restrictions for that of target polysemy resolution. Experiments show that the model achieves 62.92% monolingual information retrieval, which is 40.80% better than that of the select-all model. When target polysemy resolution is added, the retrieval performance represents approximately a 10.11% increase over that of the model which resolves translation ambiguity only.