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研究生:巫啟台
研究生(外文):Chi-Tai Wu
論文名稱:文件之關聯資訊萃取及其概念圖自動建構
論文名稱(外文):Relation Extraction and Concept Map Construction in Text Documents
指導教授:蔣榮先蔣榮先引用關係
指導教授(外文):Jung-Hsien Chiang
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
畢業學年度:90
語文別:中文
論文頁數:59
中文關鍵詞:文件探勘資訊萃取概念圖
外文關鍵詞:Information ExtractionText MiningConcept Map
相關次數:
  • 被引用被引用:62
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  • 下載下載:281
  • 收藏至我的研究室書目清單書目收藏:4
隨著電腦及網路設備的快速發展與普及,電子化資訊的傳遞與儲存也逐漸的取代了一些傳統的紙上作業方式,大量的電子資訊雖然是豐富的分析資料來源,卻也是單純以人力處理的方式所無法負荷的。本論文提出一個基於資訊萃取的文件概念圖分析架構,利用自然語言處理中的詞性標記技術提供文件在語義層次的資訊,先找出文件中專有名詞類型的重要項目詞彙,再以關聯樣版將文件中的關聯資訊萃取出來,提供了一個有效的從文字性資料中發掘關聯資訊的方法。其中,從文件中萃取出來「項目→關聯描述→項目」形式的關聯資訊,也對資料集中項目的關聯狀況提供了比數值性關聯更明確的解釋。而將項目間的關聯資訊轉換後以概念圖的形式輸出,則讓使用者可以更容易的看出項目間的關聯狀況,提供了使用者一個瀏覽關聯資訊的較佳途徑。
Due to the rapid advancement and popularization of computer hardware and network equipment, traditional paper operations are replacing by electronic transmission and storage ways gradually. Although large amount of electronic information provides rich resource for analyzing, we could not accomplish these works by hand. In this thesis, we propose an Information Extraction based approach to discover relation in text documents. In our approach, we use the part-of-speech tagger to acquire semantic information in documents and begin with extracting meaningful terms from sentences according to the part-of-speech tags. Then we extract the relation information in documents by using the Relation Template. The extracted information, which is in the form of “Term→Relation Description→Term”, provides more clear comment than the relation degree for the relationships in the dataset. Further more, transforming the relation information among terms to the manner of Concept Maps gives people an easier and better way for browsing the relation information in the dataset.
第一章 導論 1
1.1 概論 1
1.2 研究動機 2
1.3 解決方法 3
1.4 論文架構 3
第二章 相關研究 4
2.1 資料探勘(Data Mining) 4
2.2 資訊萃取(Information Extraction) 5
2.3 文件分析系統 7
2.3.1 TextAnalyst 7
2.3.2 ClearResearch 8
2.4 概念圖(Concept Map) 10
第三章 基於資訊萃取之文件概念圖分析 14
3.1 基於資訊萃取之文件概念圖分析架構 14
3.1.1 文件前處理 15
3.1.2 關聯資訊萃取 16
3.1.3 概念圖建構 17
第四章 文件中關聯資訊的萃取 18
4.1 項目的選取與標示 18
4.2 關聯樣版(Relation Template)定義 20
4.3 關聯樣版的產生 23
4.4 關聯樣版比對程序 25
第五章 關聯資訊的概念圖表示法 29
5.1 概念圖中概念及關聯的定義 29
5.2 將關聯資訊轉換成概念圖 30
5.3 概念圖的視覺化 32
第六章 實驗與結果分析 36
6.1 實驗資料集介紹 36
6.2 項目萃取結果與分析 37
6.3 關聯樣版設定及比對結果 39
6.4 概念圖結果分析 41
6.5 與資料探勘結果的比較 43
第七章 結論與未來展望 45
7.1 結論 45
7.2 未來展望 45
參考文獻 47
附錄一 詞性標記列表 49
附錄二 樣版比對函式虛擬碼 51
附錄三 實驗結果中「America Online」的關聯資訊 55
附錄四 實驗結果中包含「America Online」的關聯法則 57
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