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Identification of Basic Phrases for Kazakh Language using Maximum Entropy Model
BibTeX
@MISC{Altenbek_identificationof,
author = {Gulila Altenbek and Xiaolong Wang and Gulizhada Haisha},
title = {Identification of Basic Phrases for Kazakh Language using Maximum Entropy Model},
year = {}
}
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Abstract
This paper proposes the definition, classification and structure of the Kazakh basic phrases, and sets up a framework for classifying them according to their syntactic functions. Meanwhile, the structure of the Kazakh basic phrases were analyzed; and the determination of the Kazakh basic phrases collocation and extraction of the Kazakh basic phrases based on rules were followed. The Maximum Entropy (ME) model uses for the identification of the phrases from texts and achieved a result of automatic identifica-tion of Kazakh phrases with an accuracy of 78.22 % based on rules System and additional artificial mod-ification. Design feature of this ME model join rely on templates of Kazakh Word, part of speech, affix-es. Experimental results show that the accuracy rate reached 87.89％． 1
Keyphrases
basic phrase kazakh language maximum entropy model kazakh basic phrase design feature automatic identifica-tion syntactic function kazakh basic phrase collocation model join rule system maximum entropy kazakh phrase additional artificial mod-ification experimental result kazakh word accuracy rate
