By Topic

A Lexical Knowledge Acquisition Model Using Unsupervised Learning Method

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Doo-Soon Park ; Dept. of Comput. Software, Soonchunhyang Univ., Asan, South Korea ; Wonhee Yu ; Kinam Park ; Heui-Seok Lim

This paper proposes a computational lexical entry acquisition model based on a representation model of the mental lexicon. The proposed model acquires lexical entries from a raw corpus by unsupervised learning like human. The model is composed of full-form and morpheme acquisition modules. We experimented the model with a Korean raw corpus of which size is about 16 million Korean full-forms. The experimental results show that the model successively acquires major Korean full-forms and morphemes with the average precision of 100% and 99.04%, respectively.

Published in:

Ubiquitous Information Technologies and Applications (CUTE), 2010 Proceedings of the 5th International Conference on

Date of Conference:

16-18 Dec. 2010