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研究生:林雅惠
研究生(外文):LIN, YA-HUEI
論文名稱:基於條件機率域萃取引用文獻資訊於個人著述網頁
論文名稱(外文):Mining Publication Records on Publication Pages based on Conditional Random Fields
指導教授:李漢銘李漢銘引用關係何建明何建明引用關係
指導教授(外文):Hahn-Ming LeeJan-Ming Ho
口試委員:李漢銘何建明
口試日期:2012-07-10
學位類別:碩士
校院名稱:國立臺灣科技大學
系所名稱:資訊工程系
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:2012
畢業學年度:100
語文別:英文
論文頁數:47
中文關鍵詞:條件機率域引用文獻網頁探勘
外文關鍵詞:Conditional Random FieldsPublication RecordWeb Mining
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一筆引用文獻資訊記載著作者、文獻標題、發表年份以及其它資訊。數位圖書館利用分析引用文獻提供許多的應用,例如學術社群分析,研究學者的專長分析。而我們可以從期刊網站或是研究學者的個人著述網頁上萃取引用文獻資訊以供數位圖書館利用。含有引用文獻資訊的網頁通常也含有其他資訊,例如在研究學者的個人著述網頁上可能存在個人經歷或是記錄曾經發表的演講。如何能正確的從不同的網頁(尤其是研究學者的個人著述網頁)萃取出引用文獻是個很有趣的議題,因為在一個網頁裡視覺上有規律性的相似引用文獻資訊並不一定由相似的網頁程式組成,並且不同網頁有不同的呈現方式來表達引用文獻資訊。
在此篇論文中,我們提出了一個引用文獻萃取系統,能有效的從不同的網頁上萃取出引用文獻。我們觀察含有引用文獻的網站發現在一個網頁裡的引用文獻資訊通常遵循著相似的文獻資訊排列順序,例如:“作者 標題 年份”或是“年份 標題 作者”,所以我們利用條件機率域演算法訓練出一個分析文獻資訊模型,分析在一個網頁中可能的文獻資訊排列順序,並且利用擴散概念的演算法切割出正確的引用文獻資訊的。最後相較以往的研究成果,由實驗證明我們所提出的系統的確能更準確的萃取出引用文獻資訊。
A publication record is a list of semi-structured citation strings for publications of a research institute or an individual researcher. Publication records are integrated into a digital library which becomes an important knowledge base and thereby enables a variety of applications. A publication record is usually found among other information on a publication Web page (or ”publication page” for short). It is thus an interesting problem to extract publication record from such Web pages. The problem is difficult for several reasons, e.g., flexibility in formatting the metadata of a publication as a
semi-structured citation string and flexibility in expressing the citation string visually presentation in HTML. Furthermore, two citation strings with a similar visual presentation on the same Web page may have different HTML constructs. In this paper, we present a content analysis approach, based on Conditional Random Fields and data region boundary analysis, the problem of automatically extracting publication records on a publication page. Experimental results show that our method performs well on a benchmark containing manually crafted publication pages. The precision rate and recall rate, and F-measure are 82.5%, 87.6%, and 85.0%, respectively. This is a significant improvement over previous researches.
ABSTRACT i
ACKNOWLEDGEMENTS ii
1 Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.2 Example of personal publication pages . . . . . . . . . . . . . . . . . 5
1.3 Goals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.4 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.5 Outlines of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2 Background 9
2.1 Publication Extraction and Parsing . . . . . . . . . . . . . . . . . . . 10
2.2 Information Extraction on the Web . . . . . . . . . . . . . . . . . . . 12
2.3 Conditional Random Fields based Approach . . . . . . . . . . . . . . 13
3 Publication Record Miner 14
3.1 Publication Page Segmentation . . . . . . . . . . . . . . . . . . . . . 17
3.1.1 DOM Tree Constructor . . . . . . . . . . . . . . . . . . . . . 18
iii
CONTENTS iv
3.1.2 Data Region Segmenter . . . . . . . . . . . . . . . . . . . . 19
3.2 Publication field Labeling . . . . . . . . . . . . . . . . . . . . . . . . 21
3.2.1 Content Tokenizer . . . . . . . . . . . . . . . . . . . . . . . 22
3.2.2 Feature Assigner . . . . . . . . . . . . . . . . . . . . . . . . 23
3.2.3 CRF-based Labelor . . . . . . . . . . . . . . . . . . . . . . . 25
3.3 Publication Record Extraction . . . . . . . . . . . . . . . . . . . . . 26
3.3.1 Diffusion-based Candidate Extractor . . . . . . . . . . . . . . 27
3.3.2 Publication Record Filter . . . . . . . . . . . . . . . . . . . . 29
4 Empirical Experiments and Results 30
4.1 CORA information extraction dataset . . . . . . . . . . . . . . . . . 31
4.2 Dataset P . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
4.3 Evaluation Metrics and Experiment Design . . . . . . . . . . . . . . 32
4.3.1 Results and Discussions . . . . . . . . . . . . . . . . . . . . 35
4.3.2 The Limitation of PRM . . . . . . . . . . . . . . . . . . . . . 38
5 Conclusion and Further Work 40
5.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
5.2 Further Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
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