Abstract
The focused crawler is a topic-driven document-collecting crawler that was suggested as a promising alternative of maintaining up-to-date Web document indices in search engines. A major problem inherent in previous focused crawlers is the liability of missing highly relevant documents that are linked from off-topic documents. This problem mainly originated from the lack of consideration of structural information in a document. Traditional weighting method such as TFIDF employed in document classification can lead to this problem.
In order to improve the performance of focused crawlers, this paper proposes a scheme of locality-based document segmentation to determine the relevance of a document to a specific topic. We segment a document into a set of sub-documents using contextual features around the hyperlinks. This information is used to determine whether the crawler would fetch the documents that are linked from hyperlinks in an off-topic document.
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References
De Bra, P., et al.: Information Retrieval in Distributed Hypertexts. In: Proc. 4th Int’l. Conf. Intelligent Multimedia Information Retrieval System and Management, Center of High Int’l. Studies of Documentary Information Retrieval, pp. 481–491 (1994)
Hersovici, M., et al.: The SharkSearch Algorithm-An Application: Tailored Web Site Map**. In: Proc. 8th Int’l. World Wide Web Conf., pp. 213–225 (1998)
Cho, J., et al.: Efficient Crawling through URL Ordering. Computer Networks and ISDN Systems 30, 161–172 (1998)
Chakrabarti, S., Van den Berg, M.H., Dom, B.E.: Focused Crawling: A New Approach to Topic-Specific Web Resource Discovery. Computer Networks 31(11-16), 1623–1640 (1999)
Diligenti, M., et al.: Focused Crawling Using Context Graphs. In: Proc. 26’th Int’l. Conf. Very Large Data Bases, pp. 527–534. Morgan Kaufmann, San Francisco (2000)
McCallum, A., et al.: Building Domain-Specific Search Engines with Machine Learning Techniques. In: Proc. AAAI Symp. Intelligent Agents in Cyberspace, pp. 28–39. AAAI Press, Menlo Park (1999)
Mitchell, T.M.: Machine Learning, pp. 154–199. McGraw-Hill, New York (1997)
Yang, Y., Pedersen, J.P.: A Comparative Study on Feature Selection in Text Categorization. In: Proceedings of the Fourteenth International Conference on Machine Learning, pp. 412–420 (1997)
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© 2005 Springer-Verlag Berlin Heidelberg
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Yang, J., Kang, J., Choi, J. (2005). A Focused Crawler with Document Segmentation. In: Gallagher, M., Hogan, J.P., Maire, F. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2005. IDEAL 2005. Lecture Notes in Computer Science, vol 3578. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11508069_13
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DOI: https://doi.org/10.1007/11508069_13
Publisher Name: Springer, Berlin, Heidelberg
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