SIGDOC '86 Proceedings of the 5th annual international conference on Systems documentation
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
The interaction of knowledge sources in word sense disambiguation
Computational Linguistics
Introduction to the special issue on word sense disambiguation: the state of the art
Computational Linguistics - Special issue on word sense disambiguation
Word-sense disambiguation using decomposable models
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Lexical disambiguation using simulated annealing
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 1
HLT '93 Proceedings of the workshop on Human Language Technology
Word Sense Disambiguation: Algorithms and Applications (Text, Speech and Language Technology)
Word Sense Disambiguation: Algorithms and Applications (Text, Speech and Language Technology)
A new supervised learning algorithm for word sense disambiguation
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
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This paper presents the initial stages of a WSD system based on Lexical Constellations. The system pursues two priorities: first, minimize computational costs, and second, deal with different degrees of sense granularity. Computationally, this model has the advantage of involving relatively low-dimensional feature space, because it runs on raw contextual data. We use discriminant function analysis as it allows us to compute distances between each occurrence and each semantic class; for each meaning, we determine the location of the point (group centroids) that represents the means for all variables (collocational data) and for each case we then compute the distances (of the respective case) from each of the group centroids. Finally, we classify cases as belonging to the group (meaning) to which it is closest. The transition from coarse-grained senses to finer-grained ones can be achieved by means of reiteration of the same algorithm on different levels of contextual differentiation.