Acquiring lexical knowledge using raw corpora and unsupervised clustering method

Kinam Park, Heuiseok Lim

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


In this paper, we propose a computational model for automatic acquisition of lexical knowledge based on the principles of human language information processing. The proposed model assumes a hybrid model for the human lexical representation including full-list and decomposition forms. The proposed method automatically acquires lexical entries and its grammatical knowledge by unsupervised learning techniques. For the purposes of evaluating performance of the proposed method, a large-scale corpus of over 10 million lexical was used, the lexical knowledge acquisition process was tested, and the results were analyzed.

Original languageEnglish
Pages (from-to)901-910
Number of pages10
JournalCluster Computing
Issue number3
Publication statusPublished - 2014 Sept

Bibliographical note

Funding Information:
Acknowledgements The research was supported by Soonchun-hyang University, 2013, and was supported by the Brain Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Plannig (2006-2005466).


  • Entropy
  • Lexical knowledge acquisition
  • Mental lexicon
  • SOM

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications


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