SIGDOC '86 Proceedings of the 5th annual international conference on Systems documentation
The Role of Conceptual Relation in Word Sense Disambiguation
NLDB'01 Proceedings of the 6th International Workshop on Applications of Natural Language to Information Systems
Accurate methods for the statistics of surprise and coincidence
Computational Linguistics - Special issue on using large corpora: I
Word association norms, mutual information, and lexicography
ACL '89 Proceedings of the 27th annual meeting on Association for Computational Linguistics
Lexical disambiguation using simulated annealing
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 1
Word-sense disambiguation using statistical models of Roget's categories trained on large corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
The UNED systems at Senseval-2
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
Supporting Arabic cross-lingual retrieval using contextual information
IRFC'11 Proceedings of the Second international conference on Multidisciplinary information retrieval facility
Wikification via link co-occurrence
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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We review in detail here a polished version of the systems with which we participated in the SENSEVAL-2 competition English tasks (all words and lexical sample). It is based on a combination of selectional preference measured over a large corpus and hierarchical information taken from WordNet, as well as some additional heuristics.We use that information to expand sense glosses of the senses in WordNet and compare the similarity between the contexts vectors and the word sense vectors in a way similar to that used by Yarowsky and Schuetze. A supervised extension of the system is also discussed. We provide new and previously unpublished evaluation over the SemCor collection, which is two orders of magnitude larger than SENSEVAL-2 collections as well as comparison with baselines. Our systems scored first among unsupervised systems in both tasks. We note that the method is very sensitive to the quality of the characterizations of word senses; glosses being much better than training examples.