博碩士論文 106423034 詳細資訊




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姓名 蔡丞祐(Cheng-You Tsai)  查詢紙本館藏   畢業系所 資訊管理學系
論文名稱 應用查詢擴展字詞及原始查詢字詞之語意資訊於文件重排序之方法
(The application of the semantic information of terms residing in query expansion and original query for document re-ranking)
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摘要(中) 近年來隨著網路的發展,使用者可以透過資訊檢索快速取得資訊,雖然資訊取得已變得容易,但如何更精確、有效率地讓使用者獲取所需資訊是重要的議題之一。而在相關回饋領域中,以Rocchio演算法最廣泛被應用,其分析相關與非相關文件出現頻率來產生新的查詢字詞,但Rocchio僅以字詞出現頻率作為依據,並未考量到其他字詞間的語意資訊。近年來有許多基於語意相關的研究被提出,其概念為挖掘字詞之間更深層的語意關係,因此本研究將以使用者的原始查詢以及相關回饋作為基礎,利用Word2Vec計算查詢擴展字詞間之語意相似度並萃取其概念資訊,再透過原始查詢字詞與查詢擴展之概念所隱含之語意關係計算出概念重要性,最後計算出文件與查詢擴展之概念匹配程度,用以重新排序查詢擴展之檢索結果。最後透過實驗證實,本研究所提出之方法於前五篇及前十篇準確率相較於Rocchio演算法能提升30%以及32%之效能,相較於Cai提出之方法能再提升9%與4%之效能。
摘要(英) In recent years, with the development of the Internet, users can quickly obtain information through information retrieval. But how to obtain the required information more accurately and efficiently is one of the important issues. In the field of relevance feedback, Rocchio′s query expansion is most widely used. The algorithm generates new query terms by analyzing the frequency of terms which residing in relevance and non-relevance document. However, Rocchio′s method only utilize the term frequency, and doesn′t concern semantic information between terms. Recently, the idea of semantic related study had been proposed, the concept of which is to explore the deeper semantic information between terms. Therefore, based on the user′s original query and relevance feedback, our study utilizes Word2Vec to analyze the semantic information and extract the concept of query expansion by using clustering algorithm, then calculate the concept importance through semantic information between terms of original query and query expansion. Finally, using concept score for document re-ranking by calculates concepts score between query expansions and documents. The result of experiments verify that the study is effective in document retrieval.
關鍵字(中) ★ 資訊檢索
★ 相關回饋
★ 查詢擴展
★ Word2Vec
★ 文件重排序
關鍵字(英) ★ Information Retrieval
★ Relevance Feedback
★ Query Expansion
★ Word2Vec
★ Document Re-ranking
論文目次 中文摘要 i
英文摘要 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 相關回饋 4
2-1-1 相關回饋背景與應用 4
2-1-2 Rocchio演算法 6
2-2 查詢擴展 7
2-2-1 局部查詢擴展 9
2-2-2 全域查詢擴展 9
2-3 分群相關研究 10
2-3-1 K-Means 10
2-3-2 Affinity Propagation 11
2-3-3 字詞語意分群 13
2-4 查詢擴展字詞資訊應用方法 13
2-5 詞嵌入 15
2-5-1 Word2Vec 15
2-5-2 CBOW 15
2-5-3 Skip-Gram 16
三、 研究方法 18
3-1 系統架構 18
3-2 方法設計 19
3-2-1 原始查詢結果處理 20
3-2-2 查詢擴展字詞之語意相似度分群處理 20
3-2-3 概念重要性處理 21
3-2-4 文件重排序處理 24
四、 實驗設計 28
4-1 實驗資料 28
4-2 實驗評估指標 30
4-3 實驗參數設計 34
4-3-1 Rocchio演算法之參數設定 34
4-3-2 概念之數量設定 35
4-3-3 重排序之參數設定 35
4-4 實驗流程 37
4-4-1 實驗一之流程 38
4-4-2 實驗二之流程 38
4-5 實驗結果 38
4-5-1 實驗一結果 38
4-5-2 實驗二結果 47
4-6 實驗結果討論 54
五、 結論 55
5-1 結論與貢獻 55
5-2 未來研究方向 56
參考文獻 57
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指導教授 周世傑(Shih-Chieh Chou) 審核日期 2019-7-23
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