| Summary: | 碩士 === 元智大學 === 資訊管理學系 === 98 === For non-native learners, distinguishing synonyms of second language is not easy. The reason is that near-synonyms may have their specific usage and syntactic constraints. Therefore, second language learners may use wrong words when composing a text even though they know the meaning of the words. For instance, although strong and powerful have similar meaning, "strong coffee" is a collocation and "powerful coffee" is an anti-collocation.
This study proposes the use of a vector space model (VSM), which has been widely used in Information Retrieval (IR) community, to distinguish among near-synonyms. The VSM is compared to two unsupervised methods: n-gram and pointwise mutual information (PMI). Experimental results show that the VSM achieved higher accuracy that both n-gram and PMI.
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