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1 PDF Automatic identification of academic articles in Japanese PDF files Teru AGATA Atsushi IKEUCHI Emi ISHIDA Michiko NOZUE Takashi KUNO Shuichi UEDA Résumé As open-access policies gain acceptance, an increasing number of researchers are contributing their papers to publicly accessible web sites (i.e. self-archiving). Theoretically, these papers are accessible from standard search engines, but they tend to be obscured by other contents on the web. The purpose of this research is to develop a system that can automatically detect Teru AGATA: Asia University, Sakai Musashino-shi, Tokyo agata@asia-u.ac.jp Atsushi IKEUCHI: Daito Bunka University Emi ISHIDA: Surugadai University Michiko NOZUE: Railway Technical Research Institute Takashi KUNO: Sakushingakuin University Shuichi UEDA: Keio University
2 PDF academic articles and/or quasi-academic articles on the web. This paper describes experiments that were conducted on the performance of various classifiers and the results are compared in terms of precision, recall, and F-measure. The classifiers use attributes such as terms in PDF files and empirical rules. The results suggest the e$ciency of a ranked output system which has several phases to identify academic articles. I. A. B. C. PDF D. II. A. PDF B. C. D. III. A. B. C. IV. A. B. C. V. A. B. I. A Budapest Open Access Initiative (BOAI) 1) 44
3 2) ) B. (JST) 4) (J-STAGE) 5) ) OAIster Google CiteSeer. IST 7) Google Scholar 8) CiteSeer.IST CiteSeer.IST 9) Google Scholar 10) Google Scholar (1) (2) C. PDF PDF PDF, HTML, XML, TeX, MS Word PDF 45
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8 PDF 3a no. 11, 2001, p. 9 3b EU - vol. 39, no. 2, p. 63 PDF Xpdf 1 2. SVM MeCab ) 2 bigram mecab, bigram bigram 50
9 III. A. 1. PDF mecab 77, ) PDF URL ac.jp URL go.jp 51
10 PDF B. SVM, AdaBoost, 3 SVM, AdaBoost (C4.5), Vote Weka (Waikato Environment for Knowledge Analysis) Weka 19) 20) Waikato Java Weka3.4.7, Weka3.5.2 Weka Weka 1. SVM Vapnik 2 21) SVM SVM AdaBoost SVM SVM light ) Weka LIBSVM ) 2. AdaBoost (Boosting) (Bagging) (ensemble learning) AdaBoost Schapire Singer AdaBoost k-nn 24) AdaBoost 25) SVM 26) AdaBoost 52
11 BoosTexter AdaBoost.MH BoosTexter 27) mecab 70 AdaBoost BoosTexter AdaBoost 3 AdaBoost. MH 2 28) AdaBoost.MH Weka AdaBoost (decision stumps) Weka BoosTexter AdaBoost 3. / (naive Bayesian classifier) naive (Bayesian Filter) Paul Graham A plan for spam 29) bsfilter 30) bsfilter Paul Graham Gary RobinsonFisher 31) bsfilter Weka NaïveBayes 4. (C4.5) (decision tree) CART 32),ID3 33),C4.5 34) Weka C4.5 J48 if-then 4 AdaBoost AdaBoost 53
12 PDF a0.5 a0.5 F 1 a1/3 F 2 P R 4 5. (Vote) Weka Vote SVM AdaBoost (100), C. (P), (R), F F 1 F 2 F F a F 1 a1/p(1a)1/r 4 (macro-averaging) IV. 611 A. 2 3 / mecab / bigram, 4 AdaBoost 3 54
13 1. 6 SVM mecab SVM 7 bigram F 1, F 2 AdaBoost AdaBoost SVM 0 F 7 N/A AdaBoost F F 1, F 2 Vote 6 F 1 F 2 SVM mecab bigram AdaBoost Round 10 mecab Round 100 mecab Round 1000 mecab SVM mecab ,891 bigram ,598 mecab bigram AdaBoost Round 10 mecab Round 100 mecab Round 1000 mecab mecab bigram ,937 55
14 PDF Vote SVM 1 SVM SVM (C4.5) SVM F 1 F (C4.5) Round AdaBoost Round Round SVM N/A.000 N/A N/A N/A Vote F 1 F 2 SVM mecab AdaBoost (R100) mecab AdaBoost (R1000) mecab bigram , AdaBoost (R1000) Vote
15 Vote F Vote F 1 AdaBoost (1000) mecab.469 F 2 19 F B SVM bigram SVM mecab bigram 9 F 1 F 2 SVM mecab bigram AdaBoost Round 10 mecab Round 100 mecab Round 1000 mecab SVM mecab ,889 bigram ,597 mecab bigram AdaBoost Round 10 mecab Round 100 mecab Round 1000 mecab mecab bigram ,937 57
16 PDF bigram F 1 SVM bigram F 2 AdaBoost 1000 AdaBoost SVM SVM F Vote F 1 F (C4.5) Round AdaBoost Round Round SVM Vote F 1 F 2 SVM bigram AdaBoost (R1000) mecab bigram AdaBoost (R1000) Vote
17 bigram SVM bigram F F 1 bigram SVM F 2 Vote C. mecab SVM PDF PDF 1. SVM SVM 6 / SVM 2 SVM
18 PDF V. A. 1. PDF PDF PDF SVM AdaBoost PDF 3 1) 2) 60
19 SVM SVM 5 Vote 3) 5 B PDF 3. 61
20 PDF 1) Budapest Open Access Initiative ) Lawrence, S. Free online availability substantially increases a paper s impact. Nature. vol. 411, no.6837, 2001, p nature. com / nature / debates / e-access / Articles / lawrence. html, http: / / www. nature. com / nature / journal / v 411 / n 6837 / full / a0.html ) EPrints. Journal PoliciesSummary Statistics So Far ) jst.go.jp/ ) J-STAGE. jstage.jst.go.jp/ )... vol. 55, no. 10, 2005, p ) CiteSeer.IST ) Google Scholar Beta. com/ ) Lawrence, S.; Giles, C.L.; Bollacker, K. Digital libraries and autonomous citation indexing. IEEE Computer. vol. 32, no. 6, 1999, p ) Google Scholar Beta ) http: / / www. openaccessjapan. com / archives / 2006 / 05 / oa1. html ) Crane, Diana. 1979, 260 p. 13) Adobe Acrobat family. adobe.co.jp/products/acrobat/ ) Glyph & Cog. Xpdf. com/xpdf/ ) papy. index.html ) Ishikawa, O. pdftrans/ ) Taku, Kudo. MeCab: Yet Another Part-of- Speech and Morphological Analyzer. chasen.org/taku/software/mecab/ ) PDF 2005, /23 p ) Department of Computer Science, University of Waikato. Weka. ac.nz/ml/weka ) Witten, Ian H.; Frank, Eibe. Data Mining: Practical Machine Learning Tools and Techniques. 2nd ed., San Francisco, Morgan Kaufmann, 2005, 525 p. 21) Vapnik, Vladimir N. The Nature of Statistical Learning Theory. 2nd ed. New York, Springer, 2000, xix, 314 p. SVM Cristianini, Nello; Shawe-Taylor, John. 2005, 252p. 22) Joachims, Thorsten. SVM light. http: / / svmlight.joachims.org/ ) Chang, Chih-Chun; Lin, Chih-Jen. LIBSVMA Library for Support Vector Machines. : ) Schapire, R.E.; Singer, Y. BoosTexter: A boosting-based system for text categorization. Machine Learning. vol. 39, no. 2/3, 2000, p ) Dietterich, Thomas G. An experimental comparison of three methods for costructing ensembles of decision trees: Bagging, boosting, and randomization. Machine Learning. vol. 40, no. 2, 2000, p ) Freund, Y.; Shapire, R. vol. 14, no. 5, 1999, p ) Allwein, E.; Schapire, R. E.; Singer, Y. BoosTexter http: / / www. research. att. com / sw/tools/boostexter/ ) Schapire, R. E. The boosting approach to machine learning: An overview. MSRI workshop on nonlinear estimation and classification. 2001, p ) Graham, Paul , p scheme / trans / spam-j. html 62
21 ) nabeken. bsfilter/bayesian spam filter ) Robinson, G. A statistical approach to the spam problem. com/article/ ) Breiman, L.; Friedman, J.; Olshen, R.; Stone, C. Classification and regression trees. Belmont, Wadsworth International Group, 1984, 358 p. 33) Quinlan, J. R. Induction of decision trees. Machine Learning. vol. 1, no. 1, 1986, p ) Quinlan, J. R. AI 1995, 293 p. SVM 63
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