Context-sensitive learning methods for text categorization
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Homonymy and polysemy in information retrieval
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Learning trees and rules with set-valued features
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Relational Data Model in Document Hierarchical Indexing
PorTAL '02 Proceedings of the Third International Conference on Advances in Natural Language Processing
TDS '00 Proceedings of the Third International Workshop on Text, Speech and Dialogue
Automatic Topic Identification Using Ontology Hierarchy
CICLing '01 Proceedings of the Second International Conference on Computational Linguistics and Intelligent Text Processing
DEXA '00 Proceedings of the 11th International Conference on Database and Expert Systems Applications
A Competitive Term Selection Method for Information Retrieval
CICLing '07 Proceedings of the 8th International Conference on Computational Linguistics and Intelligent Text Processing
Topic selection of web documents using specific domain ontology
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
Unsupervised text classification using kohonen's self organizing network
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
A method of rapid prototyping of evolving ontologies
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
The method for the unknown word classification
PKAW'06 Proceedings of the 9th Pacific Rim Knowledge Acquisition international conference on Advances in Knowledge Acquisition and Management
MFSRank: an unsupervised method to extract keyphrases using semantic information
MICAI'11 Proceedings of the 10th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
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A statistical method of document classification driven by a hierarchical topic dictionary is proposed. The method uses a dictionary with a simple structure and is insensible to inaccuracies in the dictionary. Two kinds of weights of dictionary entries, namely, relevance and discrimination weights are discussed. The first type of weights is associated with the links between words and topics and between the nodes in the tree, while the weights of the second type depend on user database. A common sense-complaint way of assignment of these weights to the topics is presented. A system for text classification Classifier based on the discussed method is described.