Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Learning dictionaries for information extraction by multi-level bootstrapping
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Three generative, lexicalised models for statistical parsing
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Integrating multiple knowledge sources to disambiguate word sense: an exemplar-based approach
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
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
Word sense disambiguation using Conceptual Density
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
HLT '93 Proceedings of the workshop on Human Language Technology
Verb class disambiguation using informative priors
Computational Linguistics
A Network Analysis Model for Disambiguation of Names in Lists
Computational & Mathematical Organization Theory
Probabilistic network models for word sense disambiguation
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
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In this paper, word sense disambiguation (WSD) accuracy achievable by a probabilistic classifier, using very minimal training sets, is investigated. We made the assumption that there are no tagged corpora available and identified what information, needed by an accurate WSD system, can and cannot be automatically obtained. The lesson learned can then be used to focus on what knowledge needs manual annotation. Our system, named Bayesian Hierarchical Disambiguator (BHD), uses the Internet, arguably the largest corpus in existence, to address the sparse data problem, and uses WordNet's hierarchy for semantic contextual features. In addition, Bayesian networks are automatically constructed to represent knowledge learned from training sets by modeling the selectional preference of adjectives. These networks are then applied to disambiguation by performing inferences on unseen adjective-noun pairs. We demonstrate that this system is able to disambiguate adjectives in unrestricted text at good initial accuracy rates without the need for tagged corpora. The learning and extensibility aspects of the model are also discussed, showing how tagged corpora and additional context can be incorporated easily to improve accuracy, and how this technique can be used to disambiguate other types of word pairs, such as verb-noun and adverb-verb pairs.