HMM Specialization with Selective Lexicalization

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We present a technique which complements Hidden Markov Models by incorporating some lexicalized states representing syntactically uncommon words. Our approach examines the distribution of transitions, selects the uncommon words, and makes lexicalized states for the words. We performed a part-of-speech tagging experiment on the Brown corpus to evaluate the resultant language model and discovered that this technique improved the tagging accuracy by 0.21% at the 95% level of confidence.
7 pages, 6 figures

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Computation and Language, Machine Learning, I.2.6; I.2.7

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http://salesiana.dossiersoluciones.com/handle/123456789/33347

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/cs/9912016
 http://arxiv.org/abs/cs/9912016
 Proceedings of the 1999 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora, pp.121-127, 1999

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arXiv