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Permanent link (DOI): https://doi.org/10.7939/R3S602

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Classifying Websites into Non-topical Categories Open Access

Descriptions

Other title
Subject/Keyword
non-topical categories
co-training
website classification
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Thapa, Chaman
Supervisor and department
Rafiei, Davood (Computing Science)
Zaiane, Osmar (Computing Science)
Examining committee member and department
Sander, Jörg (Computing Science)
Kurgan, Lukasz (Electrical and Computer Engineering)
Department
Department of Computing Science
Specialization

Date accepted
2012-09-18T15:20:46Z
Graduation date
2012-09
Degree
Master of Science
Degree level
Master's
Abstract
With the large presence of organizations from different sectors of economy on the web, the problem of detecting which sector a given website belongs to is both important and challenging. We study the problem of classifying websites into four non-topical categories: public, private, non-profit and commercial franchise. We study textual features based on word unigrams and bigrams, syntactic features based on part-of-speech tags and named entity distribution, and structural features based on depth of websites, link structures and URL patterns. Our experiments with different sets of features in classifying websites reveal that syntactic and structural features help to improve the performance when combined with word unigrams and bigrams. The improvement is more significant when words are insufficient. Experimenting on websites related to obesity control, we compare classifiers built on words extracted from various depths of a website. Our experiments under a multi-label classification setting show that crawling words from deeper depths may not be helpful. When the number of unlabeled websites is significantly larger than the labeled ones, which is usually the case, it is beneficial if the classifiers can utilize both the labeled and unlabeled data. Based on this observation, we combine multiple sets of features using the co-training algorithm in a semi-supervised setting. Our experiments show that co-training does indeed improve the classification accuracy when multiple feature sets and few labeled samples are available for training.
Language
English
DOI
doi:10.7939/R3S602
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
Citation for previous publication
C. Thapa, O. Zaiane, D. Rafiei, and A. M. Sharma. Classifying Websites into Non-topical Categories. In Proceedings of the 14th International Conference on Data Warehousing and Knowledge Discovery, 2012.

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