Browse > Article
http://dx.doi.org/10.5909/JBE.2019.24.1.48

Effective Text Question Analysis for Goal-oriented Dialogue  

Kim, Hakdong (Department of Digital Contents, Sejong University)
Go, Myunghyun (Department of Digital Contents, Sejong University)
Lim, Heonyeong (Department of Digital Contents, Sejong University)
Lee, Yurim (Department of Artificial Intelligence and Linguistic Engineering, Sejong University)
Jee, Minkyu (Department of Software Convergence, Sejong University)
Kim, Wonil (Department of Software, Sejong University)
Publication Information
Journal of Broadcast Engineering / v.24, no.1, 2019 , pp. 48-57 More about this Journal
Abstract
The purpose of this study is to understand the intention of the inquirer from the single text type question in Goal-oriented dialogue. Goal-Oriented Dialogue system means a dialogue system that satisfies the user's specific needs via text or voice. The intention analysis process is a step of analysing the user's intention of inquiry prior to the answer generation, and has a great influence on the performance of the entire Goal-Oriented Dialogue system. The proposed model was used for a daily chemical products domain and Korean text data related to the domain was used. The analysis is divided into a speech-act which means independent on a specific field concept-sequence and which means depend on a specific field. We propose a classification method using the word embedding model and the CNN as a method for analyzing speech-act and concept-sequence. The semantic information of the word is abstracted through the word embedding model, and concept-sequence and speech-act classification are performed through the CNN based on the semantic information of the abstract word.
Keywords
Intent-Analysing; Goal-oriented Dialogue; CNN(Convolutional Neural Network); concept-sequence; speech-act;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Kyung-soon Lee, Jin-xia Huang, Oh-woog Kwon, Young-kil Kim. "A Chatter Bot for a Task-Oriented Dialogue System," KIPS Transactions on Software and Data Engineering6, No.11, 499-506, 2017.   DOI
2 Oh-Woog Kwon, Teakgyu Hong, Jin-Xia Huang and Young-Kil Kim, "An Analysis for Dialogue Processing Technologies and Service Trends of Virtual Personal Assistants," Communications of the Korean Institute of Information Scientists and Engineers, Vol.35, No.8, pp.19-27, 2017.
3 Hyun-Jung Lee, Analysis and Prediction of Speakers' Intentions in a Dialogue-based NLIDB, PhD's Thesis of Sokang University, Seoul, South Korea, 2014.
4 Natural language processing technology for dialog system development, https://m.blog.naver.com/PostView.nhn?blogId=naver_search&logNo=221027662050&proxyReferer=https%3A%2F%2Fwww.google.co.kr%2F (accessed Nov. 6, 2018).
5 N. Reithinger and E. Maier, "Utilizing Statistical Dialogue Act Processing in VERBMOBIL," Proceedings of the 33rd annual meeting on Association for Computational Linguistics, Cambridge, Massachusetts, pp.116-121, 1995
6 Jong Min En, Song Wook Lee, Jung Yun Seo, "An analysis of Speech Acts for Korean Using Support Vector Machines," The KIPS Transactions : Part B, Vol.12, No.3, pp.365-368, 2005
7 Lee, Hyunjung, Kim, Harksoo, Seo, Jungyun, "Domain action classification using a maximum entropy model in a schedule management domain," AI Communications, Vol.21, No.4, pp.221-229, 2008   DOI
8 R. W. Smith and D. R. Hipp, Spoken Natural Language Dialog Systems: a Practical Approach, Oxford University Press Inc, Oxford, United Kingdom, 1995.
9 Hwang, Jaw-Won; Ko, Young-Joong, "A Korean Sentence and Document Sentiment Classification System Using Sentiment Features," Korean Institute of Information Scientists and Engineers, Vol.14, No.3, pp.336-340, 2008
10 National Law Information Center, http://www.law.go.kr/LSW/admRulLsInfoP.do?admRulSeq=2100000110550#AJAX (accessed Nov. 6, 2018).
11 Dore, J, "Holophrases, speech acts and language universals," Journal of Child Language, Vol.2, No.1, pp.21-40, 1975.   DOI
12 KKMA morpheme analyzer, http://kkma.snu.ac.kr/ (accessed Nov. 6, 2018).
13 Y. Goldberg, O. Levy, "word2vec Explained:deriving Mikolov et al.'s negative-sampling word-embedding method", arXiv preprint arXiv: 1402.3722, 2014.