Japanese-English Translation Selection Using Vector Space Model.:Using Vector Space Model
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概要
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In the SENSEVAL-2 Japanese task, senses of Japanese words are defined with respect to differences from their English translations. The Translation Memory (TM) that has pairs of their Japanese expressions and their English translations included on target Japanese words can be treated as sense categorization. Each translation of a given Japanese expression including the target word is categorized by selecting an appropriate Japanese expression from the TM. We can consider the task to be the mono-lingual problem of selecting the Japanese expression having the most similar context among candidate Japanese expressions in TM. We developed the system that tackles the task by calculating the similarity context of words co-occuring with the target word. The system calculates the similarity between the input expression and TM expressions from "context feature vectors" which characterize context words co-occurring with the target word, in each dimension, using the vector space model. Context attributes represent context word information as the combination of the syntactic/distance relation to the target word and the morphological/semantic attributes of the context word itself. They enable us to handle various context characteristics in a unified manner. The system participating in SENSEVAL-2 achieved a precision and recall of 45.8%, using JUMAN+KNP as morphological/syntactic ana lyzer and NIHONGO GOI TAIKEI as thesaurus. The result shows that the semantic attributes of context words make the greatest contribution to the performance, and that those of the dependency relation make a limited contribution.
- 言語処理学会の論文
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