Jeongrae Kim, Han-joon Kim, Jeong Kyoung Hee, Journal of Internet Computing and Services, Vol. 20, No. 2, pp. 93-100, Apr. 2019
10.7472/jksii.2019.20.2.93, Full Text:  HTML
Keywords: Job placement, Chatbot, Short text, Classification

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.

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