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http://dx.doi.org/10.7472/jksii.2019.20.2.93

Short Text Classification for Job Placement Chatbot by T-EBOW  

Kim, Jeongrae (School of Electrical and Computer Engineering, University of Seoul)
Kim, Han-joon (School of Electrical and Computer Engineering, University of Seoul)
Jeong, Kyoung Hee (Powder Research Institute, EngTech CO.)
Publication Information
Journal of Internet Computing and Services / v.20, no.2, 2019 , pp. 93-100 More about this Journal
Abstract
Recently, in various business fields, companies are concentrating on providing chatbot services to various environments by adding artificial intelligence to existing messenger platforms. Organizations in the field of job placement also require chatbot services to improve the quality of employment counseling services and to solve the problem of agent management. A text-based general chatbot classifies input user sentences into learned sentences and provides appropriate answers to users. Recently, user sentences inputted to chatbots are inputted as short texts due to the activation of social network services. Therefore, performance improvement of short text classification can contribute to improvement of chatbot service performance. In this paper, we propose T-EBOW (Translation-Extended Bag Of Words), which is a method to add translation information as well as concept information of existing researches in order to strengthen the short text classification for employment chatbot. The performance evaluation results of the T-EBOW applied to the machine learning classification model are superior to those of the conventional method.
Keywords
Job placement; Chatbot; Short text; Classification;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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1 SY Lee, "A text-based artificial intelligence chatbot definition and use case", Yonsei University 4th Industrial Revolution Brief, No.6, 2018. http://4ir.yonsei.ac.kr/
2 Ministry of Employment and Labor, "Employment and Labor Policy", 2018. http://www.moel.go.kr/info/publict/publictList.do
3 M Yan, P Castro, et al, "Building a chatbot with serverless computing", Proceedings of the 1st International Workshop on Mashups of Things and APIs. ACM, 2016. http://dx.doi.org/10.1145/3007203.3007217
4 JS Hwang and JY Oh, "Beyond the Mobile Age and into the AI Age", IT & Future Strategy, Korea Information Technology Agency, No.7, 2010.
5 B Sriram, D Fuhry, et al, "Short text classification in twitter to improve information filtering", Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval. ACM, 2010. http://dx.doi.org/10.1145/1835449.1835643
6 J Tang, X Wang, et al, "Enriching short text representation in microblog for clustering", Frontiers of Computer Science Vol.6, No.1, pp.88-101, 2012.
7 HJ Kim and JY Chang, "A Semantic Text Model with Wikipedia-based Concept Space", The Journal of Society for e-Business Studies, Vol.19, No.3, pp.107-123, 2014. http://dx.doi.org/10.7838/jsebs.2014.19.3.107
8 PN Tan, M Steinbach, and V Kumar, "Introduction To Data Mining", Boston: Pearson Addison Wesley, 2006.
9 R Navigli, "Word sense disambiguation: A survey" ACM computing surveys (CSUR), Vol.41, 2, 2009.
10 T Joachims, "A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization" No. CMU-CS-96-118. Carnegie-mellon univ pittsburgh pa dept of computer science, 1996.
11 DM Christopher, R Prabhakar, and S Hinrich, "Introduction to Information Retrieval", Cambridge University Press, 2008. https://nlp.stanford.edu/IR-book/
12 R Xu and DC Wunsch, "Clustering algorithms in biomedical research: a review", IEEE Reviews in Biomedical Engineering, Vol.3, pp.120-154, 2010. https://doi.org/10.1109/RBME.2010.2083647   DOI
13 Y Yang, "An evaluation of statistical approaches to text categorization", Information retrieval, Vol.1, 1-2, pp.69-90, 1999.   DOI