臺灣博碩士論文加值系統

在資訊檢索領域中，相關回饋演算法是從使用者所回傳的相關文件清單中，萃取重要字詞作為回饋的特徵值，常使用向量空間模型（Vector Space Model）來表示文件之字詞特徵，然而此方法只考慮字詞出現的頻率，而未考量到字詞和文件間存在之語意關係，並且對於原始查詢字詞之語意資訊未加以利用，而近年來語意搜索（Semantic search）的研究陸續被提出，目的是挖掘字詞間隱含的語意關係。因此，本研究發展一套基於語意資訊之文件特徵擷取方法，以主題模型萃取隱含於相關文件與非相關文件中之主題資訊，並擷取出較能代表使用者資訊需求之主題字詞，再使用神經網路模型Word2Vec來分析原始查詢字詞與主題字詞間之語意資訊，也同時考量主題字詞之字詞出現情況（Term-appearance situation），最終給予不同主題字詞適當的權重。實驗結果表明，本研究提出之方法的分類準確率相較於BASELINE提升27個百分點，可以找出具代表性之重要主題字詞，進而檢索出更符合使用者資訊需求之文件。

In the field of information retrieval, the relevant feedback algorithm extracts important words as feedback feature values from the list of related documents returned by the user. The vector space model is often used to represent the word features of the document. However, this method only considering the frequency of occurrence of words, but not considering the semantic relationship between words and document, and the semantic information of the original query words is not used. And the research on semantic search has been proposed in recent years, the purpose is to explore the implicit semantic relationship between words. Therefore, this study develops a document feature extraction method based on semantic information, extracts the topic information implicit in related documents and non-related documents, and extracts the topic words that are more representative of users' information needs. Then use the neural network model Word2Vec to analyze the semantic information between the original query words and the topic words, and also consider the term-appearance situation of the topic words, and finally give appropriate weights to different topic words.
The experimental results show that the classification precision of our proposed method is 27 percentage points higher than that of BASELINE, and it can find representative and important topic words, and then retrieve the documents that are more in line with the user's information needs.

論文摘要 i
Abstract ii
目錄 iv
圖目錄 vi
表目錄 viii
一、 緒論 1
1-1 研究背景與動機 1
1-2 研究目的 2
1-3 研究範圍與限制 2
1-4 論文架構 3
二、 文獻探討 4
2-1 向量空間模型 4
2-2 相關回饋 5
2-3 主題萃取 7
2-4 字詞敏感度 8
2-5 Word2Vec 9
2-6 應用相關回饋資訊於文件分類之研究 9
2-6-1 應用向量空間模型 10
2-6-2 利用關聯規則與潛在語意分析之特徵擷取方法 11
三、 研究方法 12
3-1 系統架構 12
3-2 方法設計 13
3-2-1 萃取重要主題字詞 14
3-2-2 調整主題字詞權重 14
3-2-3 文件分類器 17
四、 實驗設計 18
4-1 實驗資料 18
4-2 實驗評估指標 21
4-3 實驗參數設定 22
4-4 實驗設計與流程 23
4-4-1 實驗一: 萃取重要主題字詞 23
4-4-2 實驗二: 調整主題字詞權重 29
4-5 實驗結果 33
4-5-1 實驗一結果 33
4-5-2 實驗二結果 33
4-6 實驗結果分析及討論 36
五、 結論 40
5-1 結論和貢獻 40
5-2 未來研究方向 41
參考文獻 42

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