Abstract
This paper is concerned with the problem of morphological ambiguities using a Markov process. The problem here is to estimate interferent solutions that might be derived from a morphological analysis. We start by using a Markov chain with one long sequence of transitions. In this model the states are the morphological features and a sequence correponds to a transition from one feature to another. After having observed an inadequacy of this model, one will explore a nonstationary hidden Markov process. Among the main advantages of this latter model we have the possibility to assign a type to a text, given some training samples. Therefore, a recognition of “style” or a creation of a new one might be developped.
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References
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© 1994 Springer-Verlag New York, Inc.
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Bouchaffra, D., Rouault, J. (1994). Capturing observations in a nonstationary hidden Markov model. In: Cheeseman, P., Oldford, R.W. (eds) Selecting Models from Data. Lecture Notes in Statistics, vol 89. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-2660-4_27
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DOI: https://doi.org/10.1007/978-1-4612-2660-4_27
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-94281-0
Online ISBN: 978-1-4612-2660-4
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