Paper: Identification of Basic Phrases for Kazakh Language using Maximum Entropy Model

ACL ID C14-1095
Title Identification of Basic Phrases for Kazakh Language using Maximum Entropy Model
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2014
Authors

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??