A lexical knowledge acquisition model using unsupervised learning method

Doo Soon Park, Wonhee Yu, Kinam Park, Heui Seok Lim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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 fullforms and morphemes with the average precision of 100% and 99.04%, respectively.

Original languageEnglish
Title of host publication2010 Proceedings of the 5th International Conference on Ubiquitous Information Technologies and Applications, CUTE 2010
DOIs
Publication statusPublished - 2010 Dec 1
Event5th International Conference on Ubiquitous Information Technologies and Applications, CUTE 2010 - Sanya, China
Duration: 2010 Dec 162010 Dec 18

Other

Other5th International Conference on Ubiquitous Information Technologies and Applications, CUTE 2010
CountryChina
CitySanya
Period10/12/1610/12/18

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Keywords

  • Lexical acquisition
  • Lexical entry
  • Unsupervised learning

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems

Cite this

Park, D. S., Yu, W., Park, K., & Lim, H. S. (2010). A lexical knowledge acquisition model using unsupervised learning method. In 2010 Proceedings of the 5th International Conference on Ubiquitous Information Technologies and Applications, CUTE 2010 [5678174] https://doi.org/10.1109/ICUT.2010.5678174