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Authors: F. Canan Pembe 1 and Tunga Güngör 2

Affiliations: 1 Boğaziçi University; Istanbul Kultur University, Turkey ; 2 Boğaziçi University, Turkey

Keyword(s): Machine learning, Document structure, World Wide Web, Hypertext Markup Language.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Data Manipulation ; Evolutionary Computing ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Soft Computing ; Symbolic Systems

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.

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Paper citation in several formats:
Canan Pembe, F. and Güngör, T. (2010). A TREE LEARNING APPROACH TO WEB DOCUMENT SECTIONAL HIERARCHY EXTRACTION. In Proceedings of the 2nd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-674-021-4; ISSN 2184-433X, pages 447-450. DOI: 10.5220/0002590004470450

@conference{icaart10,
author={F. {Canan Pembe}. and Tunga Güngör.},
title={A TREE LEARNING APPROACH TO WEB DOCUMENT SECTIONAL HIERARCHY EXTRACTION},
booktitle={Proceedings of the 2nd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2010},
pages={447-450},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002590004470450},
isbn={978-989-674-021-4},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 2nd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - A TREE LEARNING APPROACH TO WEB DOCUMENT SECTIONAL HIERARCHY EXTRACTION
SN - 978-989-674-021-4
IS - 2184-433X
AU - Canan Pembe, F.
AU - Güngör, T.
PY - 2010
SP - 447
EP - 450
DO - 10.5220/0002590004470450

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