Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Using WordNet to disambiguate word senses for text retrieval
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
Query expansion using lexical-semantic relations
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
WordNet: a lexical database for English
Communications of the ACM
Foundations of statistical natural language processing
Foundations of statistical natural language processing
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Evaluating cost-sensitive Unsolicited Bulk Email categorization
Proceedings of the 2002 ACM symposium on Applied computing
Information Retrieval
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Classification of Email Queries by Topic: Approach Based on Hierarchically Structured Subject Domain
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
A Method for Automatic Text Categorization Using Word Sense Disambiguation
ICCSA '08 Proceedings of the international conference on Computational Science and Its Applications, Part II
A Survey of Automatic Query Expansion in Information Retrieval
ACM Computing Surveys (CSUR)
Multi criteria wrapper improvements to naive bayes learning
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
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Automated Text Categorization has reached the levels of accuracy of human experts. Provided that enough training data is available, it is possible to learn accurate automatic classifiers by using Information Retrieval and Machine Learning Techniques. However, performance of this approach is damaged by the problems derived from language variation (specially polysemy and synonymy). We investigate how Word Sense Disambiguation can be used to alleviate these problems, by using two traditional methods for thesaurus usage in Information Retrieval, namely Query Expansion and Concept Indexing. These methods are evaluated on the problem of using the Lexical Database WordNet for text categorization, focusing on the Word Sense Disambiguation step involved. Our experiments demonstrate that rather simple dictionary methods, and baseline statistical approaches, can be used to disambiguate words and improve text representation and learning in both Query Expansion and Concept Indexing approaches.