Implementation of Chat-Bot System using Machine Learning and Natural Language Processing
Keywords:
Yioop, messaging, Conversation, Artificial IntelligenceAbstract
A chat-bot is a computer program that can converse with humans using artificial intelligence in messaging platforms. The goal of the project is to add a chat-bot feature and API for Yioop. discussion groups, blogs, wikis etc. Yioop provides the great help in web search portal. It has its own account management system with the ability to set up groups that have discussions boards. Groups are combined users that have permission to a group feed. The user who creates a group is the owner or Admin of the group. Posts are grouped by thread in a group containing the most recent activity at the top. The chat-bot API for Yioop will allow developers to create new chat-bots, powered by rules or artificial intelligence, that can interact like a human with users in a groups feed page. Example chat-bots that can be developed with this API is weather chat-bots or book flight chat-bots. Over past few years, messaging applications have become more popular than Social networking sites. People are using messaging applications these days such as Facebook Messenger, Skype, Viber, Telegram, Slack etc. This is making other businesses available on messaging platforms leads to proactive interaction with users about their products. To interact on such messaging platforms with many users, the businesses can write a computer program that can converse like a human which is called a chat-bot.
References
Dale, R. (2016). “The return of the chatbots”. Natural Language Engineering, 22(5), 811-817. doi: https://doi.org/10.1017/S1351324916000243.
Hinton, G. E. (2002). “Training products of experts by minimizing contrastive divergence”. Neural Computation, 14, 1711–1800.
Pawar, Harsh. “College Enquiry Chatbot Using Knowledge in Database.” International Journal for Research in Applied Science and Engineering Technology 6 (2018): 2494-2496.
Reshmi, S., &Balakrishnan, K. (2016). Implementation of an inquisitive chatbot for database supported knowledge bases. Sādhanā, 41(10), 11731178.
Salakhutdinov, R., Mnih, A., & Hinton, G. (2007, June). “Restricted Boltzmann machines for collaborative filtering”.In Proceedings of the 24th international conference on Machine learning (pp. 791-798).ACM.
Soper, T. (2017, January 25). Http://www.geekwire.com/2017/8-millionpeople-amazon-echo-customer-awareness- increases-dramatically/. Retrieved March 7, 2017, from http://www.geekwire.com/2017/8-millionpeople-amazon-echo- customer-awareness-increases-dramatically.
Sun, Y., Zhang, Y., Chen, Y., & Jin, R. (2016, September). “Conversational Recommendation System with Unsupervised Learning”.In Proceedings of the 10th ACM Conference on Recommender Systems (pp. 397-398).ACM. doi:10.1145/2959100.2959114.
Thosar, Prof. D. S. et al. “Review on Mood Detection using Image Processing and Chatbot using Artificial Intelligence.” (2018).
