臺灣博碩士論文加值系統

同義詞(Near-Synonym)不只在自然語言應用中是重要的一環，也是對第二語言學習者很重要的部分。同義詞雖然是一群意思相近的單字集合，但在特定的情況與特殊用法下，選擇錯誤的同義詞會造成句意上的誤解，甚至是整個文法錯誤，因此我們希望能夠藉由上下文的訊息，再利用系統分辨出正確的同義詞，協助外語學習者做有效率的學習。
目前為止已有許多同義詞的相關研究，這些研究的方法包含：點式交互資訊(Pointwise Mutual Information, PMI)與N連詞(N-gram)模型都是常用的方法，我們想使用與以往不同的方法來提升正確率，因此我們使用跳脫語言模型(Skip N-gram)的方法參與SemEval-2007同義詞任務，結果顯示我們提出的方法是可行的，正確率也有明顯的提升。

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

書名頁 i
論文口試委員審定書 ii
授權書 iii
中文摘要 iv
英文摘要 v
誌謝 vi
目錄 vii
表目錄 ix
圖目錄 x
第一章 緒論 1
1.1 研究背景 1
1.2 動機與目的 3
1.3 研究方法簡介 4
1.4 章節概要 5
第二章 文獻探討 6
2.1 Web 1T 5-gram 6
2.2 詞彙選擇驗證 7
2.3 同義詞研究 8
第三章 研究方法 9
3.1 資料前處理 9
3.2 方法概念簡介 11
3.3 N連詞(N-gram) 12
3.3.1 N連詞模組建立 12
3.4 跳脫語言模型(Skip N-gram) 13
第四章 實驗與結果分析 16
4.1 實驗資料-資料來源 16
4.2 實驗資料-同義詞集 17
4.3 評分方法 18
4.4 最佳召回率(Recall) 19
4.5 參與團隊系統介紹 19
4.6 實驗結果與分析 21
4.6.1 方法與語料分析 21
4.6.2 詞性分析 24
4.6.3 N-gram與Skip組合分析 27
4.6.4 正確率(Accuracy) 28
4.6.5 結果範例討論 29
第五章 結論與未來展望 32
參考文獻 33

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