Publication: A tree learning approach to web document sectional hierarchy extraction
dc.contributor.author | Pembe, F.Canan | |
dc.contributor.author | Göngör, Tunga | |
dc.date.accessioned | 2020-03-11T14:11:17Z | |
dc.date.available | 2020-03-11T14:11:17Z | |
dc.date.issued | 2010 | |
dc.description.abstract | There is an increasing availability of documents in electronic form due to the widespread use of the Internet. Hypertext Markup Language (HTML) which is mostly concerned with the presentation of documents is still the most commonly used format on the Web, despite the appearance of semantically richer markup languages such as XML. Effective processing of Web documents has several uses such as the display of content on small-screen devices and summarization. In this paper, we investigate the problem of identifying the sectional hierarchy of a given HTML document together with the headings in the document. We propose and evaluate a learning approach suitable to tree representation based on Support Vector Machines. | |
dc.identifier.isbn | 978-989-674-021-4 | |
dc.identifier.uri | https://hdl.handle.net/11413/6307 | |
dc.identifier.wos | 000392361600072 | |
dc.identifier.wos | 392361600072 | en |
dc.language.iso | en_US | tr_TR |
dc.relation.journal | CAART 2010: PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE, VOL 1: ARTIFICIAL INTELLIGENCE | tr_TR |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 United States | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/us/ | * |
dc.subject | Machine Learning | |
dc.subject | Document Structure | |
dc.subject | World Wide Web | |
dc.subject | Hypertext Markup Language | |
dc.subject | Makine Öğrenme | |
dc.subject | Belge Yapısı | |
dc.subject | Dünya Çapında Ağ | |
dc.subject | Köprü Metni Biçimlendirme Dili | |
dc.title | A tree learning approach to web document sectional hierarchy extraction | |
dc.type | Book chapter | |
dspace.entity.type | Publication | |
local.indexed.at | wos | |
local.journal.endpage | 450 | tr_TR |
local.journal.startpage | 447 |
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