Foundations of statistical natural language processing
Foundations of statistical natural language processing
Bringing order to the Web: automatically categorizing search results
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
ACM SIGKDD Explorations Newsletter
A vector space model for automatic indexing
Communications of the ACM
Machine Learning
Modern Information Retrieval
Data Mining: Introductory and Advanced Topics
Data Mining: Introductory and Advanced Topics
A Study of Approaches to Hypertext Categorization
Journal of Intelligent Information Systems
Text-Learning and Related Intelligent Agents: A Survey
IEEE Intelligent Systems
Information Retrieval on the World Wide Web
IEEE Internet Computing
Automated text summarization and the SUMMARIST system
TIPSTER '98 Proceedings of a workshop on held at Baltimore, Maryland: October 13-15, 1998
Fuzzy combinations of criteria: an application to web page representation for clustering
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part II
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The core of the Internet and World Wide Web revolution comes from their capacity to efficiently share the huge quantity of data, but the rapid and chaotic growth of the Net has extremely complicated the task of sharing or mining useful information. Each inference process, from Internet information, requires an adequate characterization of the Web pages. The textual part of a page is one of the most important aspects that should be considered to appropriately perform a page characterization. The textual characterization should be made through the extraction of an appropriate set of relevant concepts that properly represent the text included in the Web page. This paper presents a method to obtain such a set of relevant concepts from a Web page, essentially based on a relevance estimation of each word in the text of a Web page. The word-relevance is defined by a combination of criteria that take into account characteristics of the HTML language as well as more classical measures such as the frequency and the position of a word in a document. Besides, heuristic rules to obtain the most suitable fusion of criteria is achieved via a statistical study. Several experiments are conducted to test the performance of the proposed concept extraction method compared to other approaches including a commercial tool. The results obtained here exhibit a greater success in the concept extraction by the proposed technique against other tested methods.