利用負相關回饋資訊以重排序文件檢索結果

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姓名

王怡茹(Yi-ru Wang) 查詢紙本館藏

畢業系所

資訊管理學系

論文名稱

利用負相關回饋資訊以重排序文件檢索結果
(Using Non-relevant Information for Document Re-ranking)

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至系統瀏覽論文 ( 永不開放)

摘要(中)

對使用者而言，無法在檢索結果的前幾筆資料中，找到所需的資訊是一件相當困擾的事情。資訊檢索系統一直以來存在的問題就是回傳的檢索結果中包含過多不相關的文件，增加使用者查詢上不必要的負擔。在過去的研究中，雖有不少學者以相關回饋的方式來改善檢索的效率，卻未見以負相關回饋的資訊做進一步的探討。因此本研究將相關回饋之負相關文件所隱含之資訊做運用，結合TREC 6資料的特性與字詞的分佈，在傳統文件字詞權重計算方法加入字詞敏感度的概念。利用相關回饋以及負相關回饋文件之字詞出現的情況和頻率找出只在負相關的高頻特徵來建立字典，再利用字典對檢索文件中之字詞進行加權，藉此降低負相關文件與相關資訊的相似度，以幫助檢索結果的重排序，協助使用者快速找到所需的資訊。實驗結果顯示，利用本方法對文件之字詞進行權重調整，能提高檢索結果之P@10至P@100的平均準確率，優於Rocchio演算法的效能。因此，本研究驗證負相關回饋的資訊對於文件重排序是有用的。

摘要(英)

Too much non-relevant information retrieved put burden on the user. In past studies, many scholars used relevance feedback to improve document retrieval performance. This paper proposes a method to apply the information of non-relevant documents with TREC 6 data characteristics and term distributions. The proposed method uses the term-appearance situation and term frequency of non-relevant documents to find negative features to create a dictionary, and then uses features of the negative dictionary to adjust term weights of retrieved documents to reduce the weights of non-relevant documents for re-ranking the search result. We compare the proposed method to Rocchio algorithm, the results of our experiment show that P@10 to P@l00 of our purpose method significantly outperforms Rocchio. Therefore, our study verifies that the information of non-relevance feedback can be useful for document re-ranking.

關鍵字(中)

★ 文件重排序
★ 資訊檢索
★ 負相關回饋

關鍵字(英)

★ Information retrieval
★ Non-relevance feedback
★ Document re-ranking

論文目次

目錄 I
圖目錄 II
表目錄 III
第一章　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
第二章　文獻探討 4
2.1 資訊檢索 4
2.2 相關回饋 5
2.3 字詞敏感度 6
2.4 負相關回饋之相關研究 7
第三章　系統設計 12
3.1 系統架構 12
3.2 特徵擷取 13
3.3 特徵分類 13
3.4 調整文件權重 15
3.5 文件重排序 18
第四章　系統實作與驗證 20
4.1 實驗資料說明 20
4.2 實驗評估準則 26
4.3 實驗設計與流程 28
4.4 實驗結果與分析 30
第五章　結論 38
5.1 結論與貢獻 38
5.2 未來研究方向 39
參考文獻 41

參考文獻

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指導教授

周世傑(Shih-chieh Chou)

審核日期

2011-7-5

推文