教師情報を必要としないWebページ群のコンテンツ自動抽出ツールの提案
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- あまめ ことじ
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1 DEIM Forum 2009 A8-4 Web CMS Web Web Web Web Web Web Web Web,,, HTML, Web, Web, Primary Content Extraction from Web Pages without Training Data Abstract Mitsuo YOSHIDA and Mikio YAMAMOTO College of Information Sciences, and Graduate School of Systems and Information Engineering University of Tsukuba, Tennodai Tsukuba Ibaraki JAPAN In recent years, the proportion of primary content in a Web page has been decreasing as content management systems (CMS s continue to spread, because CMS s automatically and excessively add unnecessary parts such as menus, copyright displays and so on into the Web page. In this paper, we propose a simple and training data-less method extracting the primary content from a collection of Web pages. We regard a Web page as a set of blocks (minimum unit of primary or non-primary content, and assume that blocks of the primary content are unique and those of non-primary content aren t. method using real Web pages of the news sites in Japanese and English. Key words Science, Data mining We describe experimental results to show performance of the Primary Content Extraction, Unsupervised, Semi-structured Data, HTML, Web and Internet, Web 1. Web Google Web 1 [1] Web CMS Content Management System 1 CMS Web Web 1 Web
2 1 2 Web Web Web Web Web 1 Web Web Web Web Web Web 2. Web Bing [2] Web Web [3] Web DOM DOM 2 /TKY html DOM Web Web Web Lin [4] Web Debnath [5] IBDF Inverse Block Document Frequency 2 1 tag-set Web TABLE TABLE 2 IBDF Web Web Web Web W3C World Wide Web Consortium Web Web 3. Web 3. 1 Web Web Web
3 Web 3 Web Web Web Web Web 3. 2 Web Web Web Web 1 Web Web Web 4 Step.1 [Web ] Web Step.2 [ ] Web Step.3 [] Step.2 Step.4 [ ] Step.3 Web Step.5 [ ] Step.4 Web 3. 3 Web Web Web Web Web S S = {D 1, D 2, D 3,..., D N } D i(1 < = i < = N Web Adblock (Firefox Add-ons Web SGML Standard Generalized Markup Language HTML DOM DOM Web 2 HTML 3 2 DOM DOM 3 Web Web HTML WWW W3C World Wide Web Consortium W3C HTML Web H1, P, DIV, TABLE FONT, STRONG, A [6] <body> <div> <p>text 1</p> <div> <div> <a href= # title= a-title text >Text 2</a> <script>code</script> </body> 3 P(1 TEXT(1 2 HTML BODY DIV(1 DIV(2 DIV(3 IMG(1 IMG(2 IMG(3 A SCRIPT TEXT(2 CODE 2 HTML DOM 3 DOM 4 5
4 SCRIPT, STYLE 2 BODY HTML Web D i(1 < = i < = N B ij(1 < = i < = N, 1 < = j < = M i B ij = (b ij1 b ij2 b ij3... b ijl (1 < = i < = N, 1 < = j < = M i 4 P(1 TEXT(1 BODY DIV(1 DIV(2 DIV(3 IMG(1 IMG(2 IMG(3 A SCRIPT TEXT(2 CODE 3 DOM 5 Web S Web D i(1 < = i < = N D i = {B i1, B i2, B i3,..., B imi } (1 < = i < = N B ij(1 < = i < = N, 1 < = j < = M i Web title, alt title, alt IMG 4 HTML <a> <body> a-title text text 1 1. <p>text 1</p> 2. <div> 3. <div> 4. <div> <a href= # title= a-title text >Text 2</a> 5. <body></body> 5 2 HTML 5 b ijk (1 < = i < = N, 1 < = j < = M i, 1 < = k < = L Web Web N L Web S Web 6 B ij(1 < = i < = N, 1 < = j < = M i B kl (1 < = k < = N, 1 < = l < = M k Sim(B ij, B kl Sim(B ij, B kl = Bij B kl B ij B kl Sim(B ij, B nm Block(1 1 Block(1 2 Block(1 i 同じかどうかじかどうか比較 Block(2 1 Block(2 2 Block(2 j Block(n 1 Block(n 2 Block(n k Web Page 1 Web Page 2 Web Page n Web Web Web Precision Recall F F-measure Perfect-matching Web N
5 1 5 <a> <body> <div> <img> <p> a-title text img-alt text text 1 text R P recision = R N C Recall = R C F F F R N C F -measure = = 2 precision recall precision + recall R 1 2 (N + C F Web Web F Web Web N Web M P erfect-matching = M N HTML DOM Web asahi.com 4 jp 5 YOMIURI ONLINE 6 Web URL CEEK.JP NEWS 7 URL HTML CEEK.JP NEWS URL Web 4 5 ALL asahi.com jp YOMIURI ONLINE 8 8 Web / OYT1T00914.htm
6 2 F A B C D E F asahi.com jp YOMIURI ONLINE jp F Web Web Web 1 1 Web 18 Web Web 8 Web Web Web 9 9 Web Web URL jp / k0000e c.html 10 / k0000e c.html 9 jp Web 1 Web / OYT8T00266.htm
7 表4 実験結果 国内 1 サイト名 適合率 asahi.com 再現率 F 値 完全一致率 毎日 jp YOMIURI ONLINE 合計 表5 実験結果 国内 2 サイト名 適合率 ALL 再現率 F 値 完全一致率 ど影響を与えていないことがわかる 4. 4 海外のニュースサイトを対象とした実験結果 使 用 し た デ ー タ セット の 詳 細 は 表 6 の 通 り で あ る CNN.com 注 12 の各 Web ページの URL は Google News 英語 版 注 13 から取得し その URL のリストを基に HTML ファ イルを取得した Google News から URL を取得する際は ド メインのみを指定し 注 14 Web ページの内容にばらつきが出る ようにしている ただし 閲覧者がコメントを付けられる Blog 形式のページは人手により除外している 実験結果を表 7 に示す 図 12 注 15 はコンテンツ自動抽出を 行った Web ページの例である 着色部分がコンテンツを示す 実験結果より 国内のニュースサイトに比べて比較的悪い結果 を示している 特に再現率と完全一致率が悪い結果を示して いる 図 10 図 11 毎日 jp の Web ページ例 2 日付の抽出に失敗した例 が含まれない場合 日付の表現方法が限られるため他の Web ページにも出現する可能性が高くなる これを解決するために は 予め日付の表現方法を学習したモデルを準備し 日付の抽 出のみ別途抽出を行うという方法が考えられる 図 12 実験結果 海外 の Web ページ例 コンテンツ抽出後 また 表 4 の合計と表 5 の結果がほぼ同等であるが 抽出方 法は異なる 表 4 の合計は 各 Web サイトで Web ページ群を 作りコンテンツを抽出した結果の合計であるが 表 5 はデータ CNN.com のデータセットには 毎日 jp データセットと同様 注 12 セット全ての Web ページで 1 つの Web ページ群を作り抽出 注 13 した結果である このことから Web サイトを横断して Web 注 14 検索クエリ site:cnn.com を利用した ページ群を作りコンテンツを抽出したとしても 性能にほとん 注 15 /01/15/bp.salarycap/
8 6 CNN.com F CNN.com URL Web Web Web CNN.com Web Web Web Web Web Web Web Web [1] Jesse Alpert, Nissan Hajaj. (2008. We knew the web was big.... Official Google Blog. /we-knew-web-was-big.html, (Accessed [2] Lidong Bing, Yexin Wang, Yan Zhang, Hui Wang. (2008. Primary Content Extraction with Mountain Model. IEEE CIT2008. pp [3],. (2008. Web. 14. [4] Shian-Hua Lin, Jan-Ming Ho. (2002. Discovering Informative Content Blocks from Web Documents. In Proceedings of ACM SIGKDD 02. pp [5] Sandip Debnath, Prasenjit Mitra, Nirmal Pal, and C. Lee Giles. (2005. Automatic Identification of Informative Sections of Web Pages. IEEE Transactions on Knowledge and Data Engineering. Vol.17, No.9, pp [6] W3C. (1999. The global structure of an HTML document. HTML 4.01 Specification. /struct/global.html#h-7.5.3, (Accessed /djf500/ dowjonesdjonline FORTUNE5.htm
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