Sudan Prasad Uprety, Seung Ryul Jeong^*

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3937-3952, 2022, DOI:10.32604/cmc.2022.023127

Abstract Artificial intelligent based dialog systems are getting attention from both business and academic communities. The key parts for such intelligent chatbot systems are domain classification, intent detection, and named entity recognition. Various supervised, unsupervised, and hybrid approaches are used to detect each field. Such intelligent systems, also called natural language understanding systems analyze user requests in sequential order: domain classification, intent, and entity recognition based on the semantic rules of the classified domain. This sequential approach propagates the downstream error; i.e., if the domain classification model fails to classify the domain, intent and entity recognition fail. Furthermore, training such intelligent… More >

Mukhtar Ghaleb^1,*, Yahya Almurtadha², Fahad Algarni³, Monir Abdullah³, Emad Felemban⁴, Ali M. Alsharafi³, Mohamed Othman⁵, Khaled Ghilan⁶

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2619-2638, 2022, DOI:10.32604/cmc.2022.020358

Abstract People often communicate with auto-answering tools such as conversational agents due to their 24/7 availability and unbiased responses. However, chatbots are normally designed for specific purposes and areas of experience and cannot answer questions outside their scope. Chatbots employ Natural Language Understanding (NLU) to infer their responses. There is a need for a chatbot that can learn from inquiries and expand its area of experience with time. This chatbot must be able to build profiles representing intended topics in a similar way to the human brain for fast retrieval. This study proposes a methodology to enhance a chatbot's brain functionality… More >

Sudan Prasad Uprety, Seung Ryul Jeong^*

Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 1-11, 2022, DOI:10.32604/iasc.2022.018757

Abstract Natural Language Understanding and Speech Understanding systems are now a global trend, and with the advancement of artificial intelligence and machine learning techniques, have drawn attention from both the academic and business communities. Domain prediction, intent detection and entity extraction or slot fillings are the most important parts for such intelligent systems. Various traditional machine learning algorithms such as Bayesian algorithm, Support Vector Machine, and Artificial Neural Network, along with recent Deep Neural Network techniques, are used to predict domain, intent, and entity. Most language understanding systems process user input in a sequential order: domain is first predicted, then intent… More >