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研究生:吳旻璁
研究生(外文):Min-Cong Wu
論文名稱:結合主題資訊萃取關鍵詞和建構概念圖
論文名稱(外文):Combining Topic Information for Keywords Extraction and Concept Maps Construction
指導教授:許中川許中川引用關係
指導教授(外文):Chung-Chian Hsu
口試委員:葉惠菁、藍友烽
口試委員(外文):Hui-Chin Yeh、Yu-Feng Lan
口試日期:2015-06-16
學位類別:碩士
校院名稱:國立雲林科技大學
系所名稱:資訊管理系
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:中文
論文頁數:40
中文關鍵詞:關鍵詞萃取、概念圖、主題模型、主題網頁排名
外文關鍵詞:Keyphrase extraction、Concept map、Topic modelling、Topic PageRank
相關次數:
  • 被引用被引用:5
  • 點閱點閱:640
  • 評分評分:
  • 下載下載:19
  • 收藏至我的研究室書目清單書目收藏:1
英文是一個重要的資訊傳播橋梁,所以要如何從英文文章內汲取有用的資訊就顯得相當重要,一篇文章的重點通常可以由多個關鍵詞表示。如何在英文文章中自動探勘出關鍵詞,並有效地讓使用者明白文章的重點,是近年來一項重要的研究議題。然而,部分關鍵詞萃取方法忽略了文章和關鍵詞間主題資訊的關聯性,因此本論文使用Topic PageRank (TPR)技術萃取全文關鍵詞,該方法基於文章的關鍵詞與該文章的主題相關度來決定一個關鍵詞的權重分數。再者,也創新的將TPR運用於段落的關鍵詞萃取,再結合本論文提出的Google Rank(GR)技術萃取段落關鍵詞,稱為TGPR。最後,產生改良式的三階層概念圖,考慮不同層關鍵詞的共現程度,強化關鍵詞之關聯性,將文章主題有效地呈現。三個資料集的實驗結果顯示,TPR在萃取全文關鍵詞時的績效相對優於其他的比較方法,在萃取段落關鍵詞時的績效亦相對於其他方法優異。

English often plays an important role in information transmission. For this reason, how to extract useful message from English articles is very important. Keyphrases often represent the summary of an article. In recent year, how to mine keyphrases and effective presentation is an important research topic. However, most of the approaches ignored the relationship among the article, the topic and keyphrases. As a result, in this study we integrate the Topic PageRank(TPR) technique to the process of keyphrase extraction of an article. The idea behind TPR is if the topic of the article and a phrase matches together, the phrase shall be viewed as an important phrase for the article. Moreover, TPR is applied innovatively to keyphrase extraction of paragraphs. Finally, TPR and our Google Rank(GR) are further integrated to form a novel Topic Page Google Rank(TGPR) that builds state-of-the-art concept map. Experimental results indicate that the proposed approach outperforms other approaches on keyphrase extraction. Moreover, we propose an improved three-layer concept map which presents the extracted keyphrases to users. The improved map considers the co-occurrence relationship of the keyphrases in the second- and the third-layer so as to enhance consistency of the map.
摘要...........................................................................i
Abstract......................................................................ii
誌謝.........................................................................iii
目錄..........................................................................iv
表目錄.........................................................................v
圖目錄........................................................................vi
一、緒論.......................................................................1
二、文獻探討...................................................................4
2.1 關鍵詞萃取.................................................................4
2.2主題模型....................................................................5
2.3概念圖......................................................................6
三、方法.......................................................................8
3.1系統架構....................................................................8
3.2訓練主題模型................................................................8
3.3 預處理.....................................................................9
3.4 建構字圖..................................................................10
3.5 關鍵詞萃取................................................................12
3.6 建立概念圖................................................................15
四、實驗結果..................................................................19
4.1實驗資料集.................................................................19
4.2評估標準...................................................................20
4.3評估結果...................................................................20
4.4概念圖呈現.................................................................28
五、結論......................................................................30
六、參考文獻..................................................................31
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