A tutorial on hidden Markov models and selected applications in speech recognition
Readings in speech recognition
Regular models of phonological rule systems
Computational Linguistics - Special issue on computational phonology
Deterministic part-of-speech tagging with finite-state transducers
Computational Linguistics
A stochastic parts program and noun phrase parser for unrestricted text
ANLC '88 Proceedings of the second conference on Applied natural language processing
A simple rule-based part of speech tagger
ANLC '92 Proceedings of the third conference on Applied natural language processing
Incremental finite-state parsing
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Tagging French: comparing a statistical and a constraint-based method
EACL '95 Proceedings of the seventh conference on European chapter of the Association for Computational Linguistics
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Parallel replacement in finite state calculus
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
Look-back and look-ahead in the conversion of Hidden Markov Models into finite state transducers
NeMLaP3/CoNLL '98 Proceedings of the Joint Conferences on New Methods in Language Processing and Computational Natural Language Learning
Proceedings of the twenty-ninth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
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This paper describes the conversion of a Hidden Markov Model into a sequential transducer that closely approximates the behavior of the stochastic model. This transformation is especially advantageous for part-of-speech tagging because the resulting transducer can be composed with other transducers that encode correction rules for the most frequent tagging errors. The speed of tagging is also improved. The described methods have been implemented and successfully tested on six languages.