博碩士論文 100423042 完整後設資料紀錄

DC 欄位 語言
DC.contributor資訊管理學系zh_TW
DC.creator謝沛翰zh_TW
DC.creatorPei-Han Hsiehen_US
dc.date.accessioned2013-7-22T07:39:07Z
dc.date.available2013-7-22T07:39:07Z
dc.date.issued2013
dc.identifier.urihttp://ir.lib.ncu.edu.tw:88/thesis/view_etd.asp?URN=100423042
dc.contributor.department資訊管理學系zh_TW
DC.description國立中央大學zh_TW
DC.descriptionNational Central Universityen_US
dc.description.abstract過去在資訊檢索 (Information Retrieval) 領域中,往往都是利用字詞出現頻率來衡量使用者下達之查詢字詞與系統中文件間之關聯性,此方式存在一個問題便是系統的檢索效能決定於使用者組成的查詢字詞品質,後來有研究者提出了以相關回饋(Relevance feedback)來解決此問題,相關回饋領域在向量空間回饋模型中又以Rocchio演算法為其指標,Rocchio演算法在運作時需要正相關文件與負相關文件來達成回饋動作,本研究提出一套方法利用原始查詢字詞所隱含之語意連結關係過濾回饋回來之正相關文件,達成語意式的負相關回饋,擷取出Rocchio演算法所忽略之語意資訊。本研究方法可使回饋文件所隱含之正相關資訊更精確,並於實驗結果證實,本方法不管是在MAP、P@N、PR Curve上等等評估指標都可以與Rocchio演算法不分軒輊,並且在某些情況上甚至更佳。zh_TW
dc.description.abstractIn the past, Information retrieval system often uses term frequency to measure the correlation between user query and corpus. The main problem is that quality of the user query can affect the retrieval efficiency. Recently, researchers have proposed the using of relevance feedback in the solving of this problem. One of the popular method is Rocchio algorithm. In the relevance feedback process, Rocchio algorithm uses positive and negative document to modify the user query. Our research proposes a method to retrieve the original query’s semantic information that Rocchio algorithm was ignored to filter irrelevant terms from positive relevance feedback. The performance of our method has been evaluated in experiment. In MAP, P@N and PR Curve show that our method is as good as Rocchio algorithm, in some case even better.en_US
DC.subject相關回饋zh_TW
DC.subject向量空間模型zh_TW
DC.subject資訊檢索zh_TW
DC.subjectNGDzh_TW
DC.subject搜尋引擎zh_TW
DC.subjectRelevance Feedbacken_US
DC.subjectVector Space Modelen_US
DC.subjectInformation Retrievalen_US
DC.subjectNGDen_US
DC.subjectSearch Engineen_US
DC.title查詢與相關回饋之語詞連結關係的應用zh_TW
dc.language.isozh-TWzh-TW
DC.titleThe application of the semantic linking between the user query and relevance feedbacken_US
DC.type博碩士論文zh_TW
DC.typethesisen_US
DC.publisherNational Central Universityen_US

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