Relaxation and neural learning: points of convergence and divergence
Journal of Parallel and Distributed Computing - Neural Computing
Learning Compatibility Coefficients for Relaxation Labeling Processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
A simple rule-based part of speech tagger
ANLC '92 Proceedings of the third conference on Applied natural language processing
Part-of-speech tagging with neural networks
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
A Machine Learning Approach to POS Tagging
Machine Learning
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
A flexible POS tagger using an automatically acquired language model
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
CIAA'02 Proceedings of the 7th international conference on Implementation and application of automata
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Relaxation labelling is an optimization technique used in many fields to solve contraint satisfcation problems. The algorithm finds a combination of values for a set of variables such that satisfies -to the maximum possible degree- a set of given constraints. This paper describes some experiments performed applying it to POS tagging, and the results obtained. It also ponders the possibility of applying it to Word Sense Disambiguation.