• KIPS Transactions on Software and Data Engineering
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• v.12 no.1
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• pp.41-50
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• 2023

Recently, as the Coronavirus disease 2019 (COVID-19) prolongs along with the development of artificial intelligence technology, a non-contact society has become commonplace. Many companies are promoting digital transformation and the activation of artificial intelligence introduction to respond to this which leads to dramatic increase of demand for Chatbot. In addition, a Chatbot has reached the point of processing business transactions from responding simple inquiries. However, it is necessary to develop an API to interface with the legacy system and there are many difficulties in connecting. To solve this, it is becoming important to establish a hybrid Chatbot environment through RPA interface. Recently, the combination of RPA and Chatbot is considered an effective tool for handling many business processes. But, there are many difficulties due to the lack of interface cases and the difficulty in finding a method to development them. This study suggests a method for building a hybrid Chatbot which is an interface Chatbot(Conversational UX) and RPA(Task Automation) from the perspective of hyper-automation based on actual development cases and review of literature review is presented, so that the interface method can be understood and develop more easily. Therefore, there are implications for actively using AI Chatbot for digital transformation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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• pp.1400-1418
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• 2020

In the development of commercial promotion, chatbot is known as one of significant skill by application of natural language processing (NLP). Conventional design methods are using bag-of-words model (BOW) alone based on Google database and other online corpus. For one thing, in the bag-of-words model, the vectors are Irrelevant to one another. Even though this method is friendly to discrete features, it is not conducive to the machine to understand continuous statements due to the loss of the connection between words in the encoded word vector. For other thing, existing methods are used to test in state-of-the-art online corpus but it is hard to apply in real applications such as telemarketing data. In this paper, we propose an improved chatbot design way using hybrid bag-of-words model and skip-gram model based on the real telemarketing data. Specifically, we first collect the real data in the telemarketing field and perform data cleaning and data classification on the constructed corpus. Second, the word representation is adopted hybrid bag-of-words model and skip-gram model. The skip-gram model maps synonyms in the vicinity of vector space. The correlation between words is expressed, so the amount of information contained in the word vector is increased, making up for the shortcomings caused by using bag-of-words model alone. Third, we use the term frequency-inverse document frequency (TF-IDF) weighting method to improve the weight of key words, then output the final word expression. At last, the answer is produced using hybrid retrieval model and generate model. The retrieval model can accurately answer questions in the field. The generate model can supplement the question of answering the open domain, in which the answer to the final reply is completed by long-short term memory (LSTM) training and prediction. Experimental results show which the hybrid word vector expression model can improve the accuracy of the response and the whole system can communicate with humans.