Generalizing case frames using a thesaurus and the MDL principle
Computational Linguistics
Applied morphological processing of English
Natural Language Engineering
Does Baum-Welch re-estimation help taggers?
ANLC '94 Proceedings of the fourth conference on Applied natural language processing
Anaphora for everyone: pronominal anaphora resoluation without a parser
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Unsupervised Italian word sense disambiguation using WordNets and unlabeled corpora
WSD '02 Proceedings of the ACL-02 workshop on Word sense disambiguation: recent successes and future directions - Volume 8
MEANING: a roadmap to knowledge technologies
COLING-Roadmap '02 Proceedings of the 2002 COLING workshop: A roadmap for computational linguistics - Volume 13
Detecting a continuum of compositionality in phrasal verbs
MWE '03 Proceedings of the ACL 2003 workshop on Multiword expressions: analysis, acquisition and treatment - Volume 18
Is shallow parsing useful for unsupervised learning of semantic clusters?
CICLing'03 Proceedings of the 4th international conference on Computational linguistics and intelligent text processing
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Our system for the Senseval-2 all words task uses automatically acquired selectional preferences to sense tag subject and object head nouns, along with the associated verbal predicates. The selectional preferences comprise probability distributions over WordNet nouns, and these distributions are conditioned on WordNet verb classes. The conditional distributions are used directly to disambiguate the head nouns. We use prior distributions and Bayes rule to compute the highest probability verb class, given a noun class. We also use anaphora resolution and the 'one sense per discourse' heuristic to cover nouns and verbs not occurring in these relationships in the target text. The selectional preferences are acquired without recourse to sense tagged data so our system is unsupervised.