Grammatical category disambiguation based on second order hidden Markov model

Author

Jian, Sun ; Wei, Wang ; Yixin, Zhong

Author_Institution

Res. Center of Intelligence, Beijing Univ. of Posts & Telecommun., China

Volume

2

fYear

2001

fDate

2001

Firstpage

887

Abstract

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

Keywords

equivalence classes; grammars; hidden Markov models; linguistics; natural languages; word processing; Baum-Welch algorithm; POS tagging; context information; grammatical category disambiguation; machine translation; model parameters; parsing; part-of-speech tagging system; phrase recognition; second order hidden Markov model; untagged text; word equivalence classes; Context modeling; Equations; Hidden Markov models; Machine intelligence; Natural languages; Parameter estimation; Probability; Robustness; Statistical analysis; Tagging;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics, 2001 IEEE International Conference on

Conference_Location

Tucson, AZ

ISSN

1062-922X

Print_ISBN

0-7803-7087-2

Type

conf

DOI

10.1109/ICSMC.2001.973029

Filename

973029