Word sense disambiguation for free-text indexing using a massive semantic network
CIKM '93 Proceedings of the second international conference on Information and knowledge management
Foundations of statistical natural language processing
Foundations of statistical natural language processing
A New Czech Morphological Analyser ajka
TSD '01 Proceedings of the 4th International Conference on Text, Speech and Dialogue
Introduction to the special issue on word sense disambiguation: the state of the art
Computational Linguistics - Special issue on word sense disambiguation
Automatic word sense discrimination
Computational Linguistics - Special issue on word sense disambiguation
Word-sense disambiguation using statistical methods
ACL '91 Proceedings of the 29th annual meeting on Association for Computational Linguistics
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This paper deals with the automatic discrimination of contexts of Czech ambiguous words. The Sch眉tze's methodology was used, modified and transformed for the Czech language. This algorithm is based on vector space and clustering. The semantic discrimination could be understood as a subtask of word sense disambiguation. In this approach, the sense of word is defined as the cluster of contexts of ambiguous word. We show that Sch眉tze's method is transportable into Czech. Our results are not as good as his because we have experimented with a highly ambiguous word.