Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Automatic Generation of Hierarchical Taxonomies from Free Text Using Linguistic Algorithms
OOIS '02 Proceedings of the Workshops on Advances in Object-Oriented Information Systems
A New Approach to Factorization - Introducing Metrics
METRICS '02 Proceedings of the 8th International Symposium on Software Metrics
A corpus-based bootstrapping algorithm for Semi-Automated semantic lexicon construction
Natural Language Engineering
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
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The NLP team of LIRMM currently works on lexical disambiguation and thematic text analysis [Lafourcade, 2001]. We built a system, with automated learningcap abilities, based on conceptual vectors for meaningrep resentation. Vectors are supposed to encode ideas associated to words or expressions. In the framework of knowledge and lexical meaningre presentation, we devise some conceptual vectors based strategies to automatically construct hierarchical taxonomies and validate (or invalidate) hyperonymy (or superordinate) relations among terms. Conceptual vectors are used through the thematic distance for decision makingan d link quality assessment.