A Lexical Knowledge Acquisition Model Using Unsupervised Learning Method
Doo-Soon Park, Wonhee Yu, Kinam Park, Heuiseok Lim · 2010
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.