Abstract—Purpose of this research is to make chatbot based system to help Small and Medium Enterprise business. Initially, we build this application only to help Small and Medium Enterprise owner to monitor their business and report. Yet, we realize that we can make our chatbot to be more effective and efficient using machine learning technique. N-gram and machine learning using schema matching are embedded to the chatbot to understand user intention and correct typographical error inside the sentences. Finally, the chatbot has been successfully achieved those objectives. It can be concluded that the chatbot can drive the users’ feeling to be more convenient and help Small and Medium Enterprise owner to monitor their business.
Index Terms—Chatbot, n-gram, machine learning, schema matching, typographical error, user intention.
The authors are with Computer Science Department, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia 11480 (e-mail: email@example.com, firstname.lastname@example.org, email@example.com, firstname.lastname@example.org).
Cite: Mikael L. Tedjopranoto, Andreas Wijaya, Levi Hanny Santoso, and Derwin Suhartono, "Correcting Typographical Error and Understanding User Intention in Chatbot by Combining N-Gram and Machine Learning Using Schema Matching Technique," International Journal of Machine Learning and Computing vol. 9, no. 4, pp. 471-476, 2019.Copyright © 2019 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).