Research on Methods of Semantic Disambiguation about Natural Language Processing

Author

Guohuan, Lou ; Hao, Zhang ; Honghui, Wang

Author_Institution

Coll. of Comput. & Autom. Control, Hebei Polytech. Univ., Tangshan, China

fYear

2009

fDate

28-29 Dec. 2009

Firstpage

347

Lastpage

349

Abstract

Natural language processing is one of the most important applications in artificial intelligence (AI), while semantic disambiguation is one of branches and difficulties in natural language processing. This paper introduces three semantic disambiguation models, Bayesian model, hidden Markov model, and maximum entropy model. These three models are used to test and compare with. The results show that the correct rate of disambiguation used by Bayesian model is the best one, the other two are also well. Every model has its own advantages.

Keywords

artificial intelligence; belief networks; hidden Markov models; maximum entropy methods; natural language processing; Bayesian model; artificial intelligence; hidden Markov model; maximum entropy model; natural language processing; semantic disambiguation; Artificial intelligence; Automatic control; Bayesian methods; Context modeling; Educational institutions; Entropy; Hidden Markov models; Natural language processing; Natural languages; Probability; Bayesian Model; Hidden Markov Model; Maximum Entropy Model; semantic disambiguation;

fLanguage

English

Publisher

ieee

Conference_Titel

Wireless Networks and Information Systems, 2009. WNIS '09. International Conference on

Conference_Location

Shanghai

Print_ISBN

978-0-7695-3901-0

Electronic_ISBN

978-1-4244-5400-6

Type

conf

DOI

10.1109/WNIS.2009.21

Filename

5381966