Reacting to Unexpected Events and Communicating in Spite of Mixed Ontologies
MICAI '02 Proceedings of the Second Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
An Information Space Using Topic Identification for Retrieved Documents
CICLing '01 Proceedings of the Second International Conference on Computational Linguistics and Intelligent Text Processing
Automatic Topic Identification Using Ontology Hierarchy
CICLing '01 Proceedings of the Second International Conference on Computational Linguistics and Intelligent Text Processing
Automatic extraction and learning of keyphrases from scientific articles
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
Recognition of word collocation habits using frequency rank ratio and inter-term intimacy
Expert Systems with Applications: An International Journal
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Given a large hierarchical dictionary of concepts, the task of selection of the concepts that describe the contents of a given document is considered. The problem consists in proper handling of the top-level concepts in the hierarchy. As a representation of the document, a histogram of the topics with their respective contribution in the document is used. The contribution is determined by comparison of the document with the "ideal" document for each topic in the dictionary. The "ideal" document for a concept is one that contains only the keywords belonging to this concept, in the proportion to their occurrences in the training corpus. A fast algorithm of comparison for some types of metrics is proposed. The application of the method in a system Classifier is discussed.