Grammatical category disambiguation based on second order hidden Markov model
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Grammatical category disambiguation is an important field because of its basis in many applications, for example, parsing, machine translation, phrase recognition and so on. We put forward an improved second-order hidden Markov model that can capture more context information and develop one part-of-speech tagging system based on the model. In order to reduce the number of model parameters, word equivalence classes are used. The parameters of model are achieved by the Baum-Welch algorithm using untagged text. Results show that it improves the accuracy of tagging.
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