• Korean Journal of Cognitive Science
• /
• v.2 no.1
• /
• pp.143-165
• /
• 1990

I will discuss some aspects of the meaning of prodiscoure maker 'kulay'in Korea.This marker has been studied few scholars,since Korean lingusts did not have any interest about this category of linguistic form.Also,they did not realized the importance of discourse and discourse markers.So,we have only shallow information about prodiscourse phenomena and prodiscourse markers,too. Morphologically,kulay(그래)'could be analyzed into 'ku(그)'and 'lay(래)'and 'lay(래)'could be divided into'l(ㄹ)'and 'ay(ㅐ)' again.But I will discuss 'kulay'as one linguistic unit without divison. It will be claimed in this paper that both [prodiscoures]feature and [discourse continuity]feature can satisfactorily account for the core meaning of'kulay'.And,it will be mentioned that the marker has many kinds of specfic meaning depends on paricular discourse.Also, I would like to examine the semantic feature([prodiscourse+discourse continuity]) in many kinds of korean discourse.And I will show that some factors re;ated tp the marker's specific meaning are the meaning of preceding and following discourse and the participant's psychological attitude.The conclusion must be that the meaning of 'kulay'can help us understand certain phenomena about prodiscourse and prodiscourse markers in the korean language.Also the various meanings of 'kulay'can give more information to Applied-Korean linguistics.

• Journal of The Korean Association For Science Education
• /
• v.40 no.3
• /
• pp.321-336
• /
• 2020

We explore automated scoring models of scientific argumentation. We consider how a new analytical approach using a machine learning technique may enhance the understanding of spoken argumentation in the classroom. We sampled 2,605 utterances that occurred during a high school student's science class on molecular structure and classified the utterances into five argumentative elements. Next, we performed Text Preprocessing for the classified utterances. As machine learning techniques, we applied support vector machines, decision tree, random forest, and artificial neural network. For enhancing the identification of rebuttal elements, we used a heuristic feature-engineering method that applies experts' classification of morphemes of scientific argumentation.