tailieunhanh - Báo cáo khoa học: "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 translation ambiguity resolution, and augmented translation restrictions for target polysemy resolution. Experiments show that the model achieves of monolingual information retrieval, and is addition to the select-all model. Combining the target polysemy resolution, the retrieval performance is about increase to the model resolving translation ambiguity only. . | Resolving Translation Ambiguity and Target Polysemy in Cross-Language Information Retrieval Hsin-Hsi Chen Guo-Wei Bian and Wen-Cheng Lin Department of Computer Science and Information Engineering National Taiwan University Taipei TAIWAN . E-mail hh_chen@ gwbian denislin @ Abstract 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 of monolingual information retrieval and is addition to the select-all model. Combining the target polysemy resolution the retrieval performance is about increase to the model resolving translation ambiguity only. 1. Introduction Cross language information retrieval CLIR Oard and Dorr 1996 Oard 1997 deals with the use of queries in one language to access documents in another. Due to the differences between source and target languages query translation is usually employed to unity the language in queries and documents. In query translation translation ambiguity is a basic problem to be resolved. A word in a source query may have more than one sense. Word sense disambiguation identifies the correct sense of each source word and lexical selection translates it into the corresponding target word. The above procedure is similar to lexical choice operation in a traditional machine translation MT system. However there is a significant difference between the applications of MT and CLIR. In MT readers interpret the translated results. If the target word has more than one sense readers can disambiguate its meaning automatically. Comparatively the translated result is sent to a monolingual information retrieval system in CLIR. The target polysemy adds extraneous senses and affects the retrieval performance. Some different approaches have been