Skip N-gram modeling for Near-Synonym choice

• Main Authors: Shih-Ting Chen, 陳士婷
• Other Authors: Liang-ChihYu
• Format: Others
• Language: zh-TW
• Online Access: http://ndltd.ncl.edu.tw/handle/49303465739107534247

碩士 === 元智大學 === 資訊管理學系 === 100 === Near-synonym is not only an important thing in natural language applications, and also very important for the second language learner. Although, near-synonym represent a groups of words with similar meaning. But, in specific case and specific usage, we choice the wrong near-synonym may cause wrong meaning, even cause grammatical errors. Therefore, we hope system can use contextual information to differentiate near-synonym. So far, there are many studies about near-synonym, the methods of these studies include: PMI and N-gram modeling. We want to use different method to improve the accuracy, so we use Skip N-gram modeling for near-synonym choice in SemEval-2007 task, the results show that our proposed method is feasible and the accuracy have improved significantly