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Editorial: special issue on web content mining
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Volume 6 ,  Issue 2  (December 2004) table of contents
Pages: 1 - 4  
Year of Publication: 2004
ISSN:1931-0145
Authors
Bing Liu  University of Illinois at Chicago, Chicago, IL
Kevin Chen-Chuan-Chang  University of Illinois at Urbana-Champaign, Chicago, IL
Publisher
ACM  New York, NY, USA
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ABSTRACT

With the phenomenal growth of the Web, there is an everincreasing volume of data and information published in numerous Web pages. The research in Web mining aims to develop new techniques to effectively extract and mine useful knowledge or information from these Web pages [8]. Due to the heterogeneity and lack of structure of Web data, automated discovery of targeted or unexpected knowledge/information is a challenging task. It calls for novel methods that draw from a wide range of fields spanning data mining, machine learning, natural language processing, statistics, databases, and information retrieval. In the past few years, there was a rapid expansion of activities in the Web mining field, which consists of Web usage mining, Web structure mining, and Web content mining. Web usage mining refers to the discovery of user access patterns from Web usage logs. Web structure mining tries to discover useful knowledge from the structure of hyperlinks. Web content mining aims to extract/mine useful information or knowledge from Web page contents. For this special issue, we focus on Web content mining.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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Bergman, M. K. The Deep Web: Surfacing Hidden Value. Technical report, BrightPlanet LLC, Dec. 2000
 
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Bunescu, R., Mooney, R. Collective Information Extraction with Relational Markov Networks. ACL-2004, 2004.
 
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He, H, Meng, W., Yu, C. Wu, Z. WISE-Integrator: An Automatic Integrator of Web Search Interfaces for E-Commerce. VLDB-03, 2003.
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Kwok, C., Etzioni, O., Weld, D. Scaling Question Answering to the Web. WWW-00, 2000.
 
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Lawrie, D. J. and Croft, W. B., Generating Hierarchical Summaries for Web Searches. WWW'03, 2003.
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Nigam, K. and Hurst, M. Towards a Robust Metric of Opinion. AAAI Spring Symposium on Exploring Attitude and Affect in Text. 2004.
 
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Sarawagi, S., Cohen, W. Semi-Markov Conditional Random Fields for Information Extraction, NIPS-04, 2004.
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Wilson, T, Wiebe, J, & Hwa, R. Just How Mad are You? Finding Strong and Weak Opinion Clauses. AAAI-04, 2004.
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Yi, L., and Liu, B. Web Page Cleaning for Web Mining through Feature Weighting IJCAI-03, 2003.
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Collaborative Colleagues:
Bing Liu: colleagues
Kevin Chen-Chuan-Chang: colleagues