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研究生:李建志
研究生(外文):Chien-Chih Lee
論文名稱:應用混合式機率模型於新聞資訊檢索之研究
論文名稱(外文):News Information Retrieval Based on Probabilistic Mixture Model
指導教授:簡仁宗簡仁宗引用關係
指導教授(外文):Jen-Tzung Chien
學位類別:碩士
校院名稱:國立成功大學
系所名稱:資訊工程學系碩博士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:中文
論文頁數:68
中文關鍵詞:混合式機率模型文件檢索
外文關鍵詞:EM algorithmnews information retrievalmixture model
相關次數:
  • 被引用被引用:11
  • 點閱點閱:793
  • 評分評分:
  • 下載下載:155
  • 收藏至我的研究室書目清單書目收藏:1
隨著資訊科技的進步,資訊的量隨著逐漸增加,而使用者所面對的資訊也就越來越多。因此,若缺少幫助我們搜尋資訊的技術,資料的搜尋將會相當困難。為了解決這個問題,產生了資訊檢索 (information retrieval) 這個技術。在這種技術之下,使用者所期望的是資訊檢索系統能夠將使用者「想要」的內容搜尋出來。
本篇論文將語言模型應用於資訊檢索的領域,採用混合式機率模型(Mixture Model)來描述文件的隨機特性,由多個語言模型及 Expectation-Maximization(EM)[9]演算法經由事後機率的估算,來對每篇文章求得一組特定的參數。另外我們亦加入在資訊檢索上常見的潛在語意索引 (Latent Semantic Index, LSI ) ,將龐大訓練文集中的資訊以低維度的矩陣保存,在測試時即運用此矩陣內容來獲得長距離資訊及潛在的語意特徵。經由實驗的結果,我們發現將語言模型也視為參數,亦即以不同類別的語料庫來取代平衡語料庫並經由EM的參數估測,能更有效的提昇檢索的正確率;另將LSI的資訊加入Mixture Model中,亦能提高檢索的正確性。
Due to the rapid development of information technology, we need to explore more methodologies to resolve more difficult problem. Accordingly, it would be very important to build effective information retrieval system to obtain useful information. When using this technology, users expect this technology is helpful to search for what they really need.
Language Model is a very important technique in speech recognition and natural language processing. In this thesis, we apply the language model in the application of news information retrieval. We use the probabilistic mixture mode to characterize the documents and use and Expectation-Maximization (EM) algorithm to estimate the model parameters for each individual document. Furthermore, we combine the Latent Semantics Indexing (LSI) in the proposed model retrieval model. Using LSI, the huge training data are reduced to a low dimension vector. As a result, we can obtain long distance information and latent semantics. In the experiments, we find that the parameter for each documents achieve better performance. Also the LSI information is feasible to improve the retrieval precision accuracy.
第一章 簡介 1
第二章 傳統檢索架構介紹 4
2.1 布林模型(Boolean Model) 4
2.2 向量模型(Vector Model) 6
2.3 機率模型(Probability Model) 10
2.4 評估方法(Performance Evaluation) 11
2.4.1 Recall Rate(召回率) 12
2.4.2 Precision Rate(準確率) 12
2.4.3 Non-Interpolated Average Precision Rate 13
第三章 相關研究介紹 14
3.1 N-gram模型介紹 14
3.1.1 語音辨識 14
3.1.2 文件分類 15
3.1.3 N-gram模型之建立 16
3.1.4 N-gram 模型之評估 17
3.2 語言模型在文件檢索上之應用 19
3.3 應用HMM/N-gram架構於資訊檢索 21
第四章 混合式機率模型於資訊檢索架構 26
4.1 混合式機率模型 26
4.2 結合混合式機率模型與LSI擷取長距離資訊 34
4.2.1 潛在語意索引(LSI)簡介 34
5.3 結合LSI與混合機率模型 38
第五章 實驗 40
5.1 資料庫介紹 40
5.2 實驗結果 41
5.2.1 向量模式、HMM/N-gram及Mixture model的比較 41
5.2.2 比較分類語料庫與CKIP語料庫對檢索的影響 42
5.2.3 比較平衡語料庫與八大類語料庫對檢索效能的影響 44
5.2.4 比較混合數對檢索效能的影響 44
5.2.5 加入潛在語意資訊的檢索效能 45
第六章 展示系統 48
第七章 結論與未來展望 50
參考文獻 52
附錄 A HMM/N-gram模型參數推導 58
附錄B. 新聞文件範例 62
參考文獻
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[43]CKIP, http://godel.iis.sinica.edu.tw/, 中央究院資訊科學研究所詞庫小組。
[44]鉅亨網, http://www.cnyes.com/
[45]民視即時新聞, http://www.can.com.tw
[46]聯合新聞網,http://udnnews.com/NEWS
[47]ETtoday, http://www.ettoday.com/
[48]中時電子報,http://news.chinatimes.com/
[49]雅虎新聞, http://news.yahoo.com.tw
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