Abstract
Like transformation-based tagging, statistical (or stochastic) part-of-speech tagging assumes that each word is known and has a finite set of possible tags. These tags can be drawn from a dictionary or a morphological analysis. When a word has more than one possible tag, statistical methods enable us to determine the optimal sequence of part-of-speech tags T = t 1, t 2, t 3, ..., t n, given a sequence of words W = w 1, w 2, w 3, ...,w n.
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7.7 Further Reading
Brown, P. E., Della Pietra, V. J., Della Pietra, S. A., and Mercer, R. L. (1993). The mathematics of statistical machine translation: Parameter estimation. Computational Linguistics, 19(2):263–311.
Carlberger, J. and Kann, V. (1999). Implementing an efficient part-of-speech tagger. Software — Practice and Experience, 29(2):815–832.
Charniak, E. (1993). Statistical Language Learning. MIT Press, Cambridge, Massachusetts.
Magerman, D. (1995). Book reviews: Statistical language learning by Eugene Charniak. Computational Linguistics, 21(1):103–111.
Och, F. J. and Ney, H. (2000). Improved statistical alignment models. In Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics, pages 440–447, Hongkong.
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(2006). Part-of-Speech Tagging Using Stochastic Techniques. In: An Introduction to Language Processing with Perl and Prolog. Cognitive Technologies. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-34336-9_7
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DOI: https://doi.org/10.1007/3-540-34336-9_7
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