Abstract
This paper describes the development work of our Academic Chatbot model in identifying the user’s conversational intents patterns. We experimented using social conversation log data from WhatsApp Messenger by the academic coordinator and students during the student’s internship period. The discovered conversational patterns are used as a heuristic in building the knowledge base for the intent and entity of our Academic Chatbot model. Our preliminary findings depicted that related conversational intent patterns named Frequent Intent Pattern (FIP) was discovered with confidence value as high as 0.9 using the Sequential Pattern-Growth technique. The basis of using a Pattern-Growth pattern representation has given an insight where the chatbot can learn over time and new information can be added based on the intent pattern discovery. The outcome of this project is a customized Academic Chatbot (AcaBot) model that will be able to assist academicians and students in Academic Institution automatically and instantly 24/7 regarding relevant academic topics.
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Alias, S., Sainin, M.S., Soo Fun, T., Daut, N. (2019). Identification of Conversational Intent Pattern Using Pattern-Growth Technique for Academic Chatbot. In: Chamchong, R., Wong, K. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2019. Lecture Notes in Computer Science(), vol 11909. Springer, Cham. https://doi.org/10.1007/978-3-030-33709-4_24
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