Some advances in transformation-based part of speech tagging
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Building probabilistic models for natural language
Building probabilistic models for natural language
Machine Learning
Part-of-speech tagging with neural networks
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
Feature-rich part-of-speech tagging with a cyclic dependency network
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Part-of-speech tagging using virtual evidence and negative training
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
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In this paper we present part-of-speech taggers based on hidden Markov models, which adopt a less strict Markov assumption to consider rich contexts. In models whose parameters are very specific like lexicalized ones, sparse-data problem is very serious and also conditional probabilities tend to be estimated unreliably. To overcome data-sparseness, a simplified version of the well-known back-off smoothing method is used. To mitigate unreliable estimation problem, our models assume joint independence instead of conditional independence because joint probabilities have the same degree of estimation reliability. In experiments for the Brown corpus, models with rich contexts achieve relatively high accuracy and some models assuming joint independence show better results than the corresponding HMMs.