Techniques for automatically correcting words in text
ACM Computing Surveys (CSUR)
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Learning to resolve natural language ambiguities: a unified approach
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
A Winnow-Based Approach to Context-Sensitive Spelling Correction
Machine Learning - Special issue on natural language learning
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Automatic Rule Acquisition for Spelling Correction
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Classifier combination for improved lexical disambiguation
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Combining Trigram-based and feature-based methods for context-sensitive spelling correction
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Transformation-based learning in the fast lane
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Language independent, minimally supervised induction of lexical probabilities
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Modeling consensus: classifier combination for word sense disambiguation
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
Evaluating sense disambiguation across diverse parameter spaces
Natural Language Engineering
Combining Classifiers for word sense disambiguation
Natural Language Engineering
Verb class disambiguation using informative priors
Computational Linguistics
Web-based models for natural language processing
ACM Transactions on Speech and Language Processing (TSLP)
Modeling consensus: classifier combination for word sense disambiguation
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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This paper investigates several augmented mixture models that are competitive alternatives to standard Bayesian models and prove to be very suitable to word sense disambiguation and related classification tasks. We present a new classification correction technique that successfully addresses the problem of under-estimation of infrequent classes in the training data. We show that the mixture models are boosting-friendly and that both Adaboost and our original correction technique can improve the results of the raw model significantly, achieving state-of-the-art performance on several standard test sets in four languages. With substantially different output to Naïve Bayes and other statistical methods, the investigated models are also shown to be effective participants in classifier combination.