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Except the article forming the main content most HTML documents on the WWW contain additional contents such as navigation menus, design elements or commercial banners. In the context of several applications it is necessary to draw the distinction between main and additional content automatically. Content extraction and template detection are the two approaches to solve this task. This book gives an extensive overview and detailed description of existing and newly developed algorithms from both areas. The described content extraction algorithms are evaluated under different aspects using objective performance measures. An analysis of methods to cluster web documents according to their underlying templates completes the book. In combination with a localised crawling process this clustering analysis can be used to automatically create sets of training documents for template detection. As the whole process can be automated it allows to perform template detection on a single document, thereby combining the advantages of single and multi document algorithms.
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Except the article forming the main content most HTML documents on the WWW contain additional contents such as navigation menus, design elements or commercial banners. In the context of several applications it is necessary to draw the distinction between main and additional content automatically. Content extraction and template detection are the two approaches to solve this task. This book gives an extensive overview and detailed description of existing and newly developed algorithms from both areas. The described content extraction algorithms are evaluated under different aspects using objective performance measures. An analysis of methods to cluster web documents according to their underlying templates completes the book. In combination with a localised crawling process this clustering analysis can be used to automatically create sets of training documents for template detection. As the whole process can be automated it allows to perform template detection on a single document, thereby combining the advantages of single and multi document algorithms.
Biografía del autor
Dr. Thomas Gottron, born in 1977, studied Mathematics, Business Management and Computing Science in Mainz, Germany. He graduated in 2003 with a diploma-thesis on content management systems. Doing research in the field of content extraction, template detection and information retrieval, he received his PhD in Computing Science in 2008.
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Content Extraction: Identifying the Main Content in HTML Documents
Verlag:
Suedwestdeutscher Verlag fuer Hochschulschriften, 2009
ISBN 10: 3838104080
ISBN 13: 9783838104089
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Content Extraction | Identifying the Main Content in HTML Documents
Verlag:
Südwestdeutscher Verlag für Hochschulschriften AG Co. KG, 2015
ISBN 10: 3838104080
ISBN 13: 9783838104089
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Taschenbuch. Zustand: Neu. Content Extraction | Identifying the Main Content in HTML Documents | Thomas Gottron | Taschenbuch | 264 S. | Deutsch | 2015 | Südwestdeutscher Verlag für Hochschulschriften AG Co. KG | EAN 9783838104089 | Verantwortliche Person für die EU: Südwestdt. Verl. f. Hochschulschrift., Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu. Artikel-Nr. 101644100
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Content Extraction
ISBN 10: 3838104080
ISBN 13: 9783838104089
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Taschenbuch
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Taschenbuch. Zustand: Neu. Neuware -Except the article forming the main content most HTML documents on the WWW contain additional contents such as navigation menus, design elements or commercial banners. In the context of several applications it is necessary to draw the distinction between main and additional content automatically. Content extraction and template detection are the two approaches to solve this task. This book gives an extensive overview and detailed description of existing and newly developed algorithms from both areas. The described content extraction algorithms are evaluated under different aspects using objective performance measures. An analysis of methods to cluster web documents according to their underlying templates completes the book. In combination with a localised crawling process this clustering analysis can be used to automatically create sets of training documents for template detection. As the whole process can be automated it allows to perform template detection on a single document, thereby combining the advantages of single and multi document algorithms.Books on Demand GmbH, Überseering 33, 22297 Hamburg 264 pp. Deutsch. Artikel-Nr. 9783838104089
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