The aim of this paper is to classify the dialogue act of user response
utterances for the development of a dialogue system for Korean language
education and to select what features are appropriate for efficiently
discriminating such utterances. This p...
The aim of this paper is to classify the dialogue act of user response
utterances for the development of a dialogue system for Korean language
education and to select what features are appropriate for efficiently
discriminating such utterances. This paper proposes a dialogue tag set that
classifies the learner's utterance intent to develop a chat bot dialog system
designed to enable Korean learners to practice Korean conversation. In order to
classify the utterances automatically according to these tag set, I examine
what features are suitable for the conversation system made for Korean
education among the discriminant features used in the previous research.
For this purpose, a corpus for Korean language education was collected to
annotate the discriminant features and dialogue act. Based on the annotated
corpus, we selected the dialogue act tags that can be used in the dialogue
system for Korean language education. Rather than the traditional linguistic
works, the discriminant features were chosen for practical use in the dialogue
system for practicing Korean conversation. In case of the discriminant
features, the features that can be commonly used in both rules base – d automatic
classification and statistics-based automatic classification are selected and
analyzed.
The first chapter presents the purpose and background of this study. After
examining the discussions related to the speech, second chapter the scholarly
works on the automatic classification method. Furthermore, this chapter analyze
the corpus used in the previous research after summarizing the discriminant
features used in the study. The third chapter provides the design and
collection of dialog corpus to be used in the dialogue system for Korean language education. The fourth chapter analyses the collected response speech
data. The conclusion part gives the results of this study and future issues.