Improvement of Chatbots Semantics Using and Word Sequence Kernel: Education Chatbot as a Case Study

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Alaa A. Qaffas 1,*

1. University of Jeddah, Jeddah, Saudi Arabia

* Corresponding author.


Received: 6 Dec. 2018 / Revised: 2 Jan. 2019 / Accepted: 20 Jan. 2019 / Published: 8 Mar. 2019

Index Terms

Natural language processing, Chatbots, textual data management, textual data analysis, text similarity


Designing interactive question-response systems has become an important challenge in Artificial Intelligence which aims to build a computer program, referred to as Chatbot, able to manage an online human-computer conversation with natural language. The number of chatbots continues to increase these recent years using different languages, tools and platforms and has been used in several domains, such as marketing, education and medicine. One of the most important issues in designing chatbots is its degree of interoperability with human natural language. In this context, we propose in this work a new messenger chatbot design approach based on and Word Sequence kernel in order to improve semantics. is used to detect contexts and concepts while the Word Sequence Kernel is used as a similarity measure between textual conversations taking into account the order of appearance of words in the conversation. A testing educate chatbot has been build which aims to provide FAQBot system for university students and acts as undergraduate advisor in student information desk. The performance of the proposed chatbot was compared to conventional messenger chatbots and showed better results.

Cite This Paper

Alaa A. Qaffas, " Improvement of Chatbots Semantics Using and Word Sequence Kernel: Education Chatbot as a Case Study", International Journal of Modern Education and Computer Science(IJMECS), Vol.11, No.3, pp. 16-22, 2019.DOI: 10.5815/ijmecs.2019.03.03


[1]IBM 2018. Overview of Watson assistant. https://console.bluemix. net/docs/services/conversation/index.html, 2018.
[2]Jerome R Bellegarda. Spoken language understanding for natural interaction: The siri experience.,. In Natural Interaction with Robots, Knowbots and Smartphones, pages 3–14, 2005.
[3]N. Cancedda, E. Gaussier, C. Goutte, and J.M. Renders. Word-sequence kernels. Journal of Machine Learning Research, 3:1059–1082, 2003.
[4]R. Carpenter and J. Freeman. Computing machinery and the individual: the personal turing test,, 2005.
[5]J. Constine. Facebook launches messenger plat- form with chatbots.,. [online] Available at: messenger/, 2016.
[6]Brooke Crothers. Google now reporting self-driving car accidents: Her, it’s not the car’s fault., Jun 8, 2015.
[7]Kumar R. Sarls T. Dasgupta, A. Fa sparse johnson: Linden strauss transform. In in Proceedings of the forty-second acm symposium on theory of computing, pages 341–350, 2010.
[8]Dumais S. T. Furnas G. W. Landauer T. K. Harsh- man R. Deerwester, S. Indexing by latent semantic analysis, Journal of the American society for information science, 41(6), 1990.
[9]David D. Lewis, Yiming Yang, Tony G. Rose, and Fan Li. Rcv1: A new benchmark collection for text categorization research. J. Mach. Learn. Res., 5:361– 397, 2004.
[10]H. Lodhi, N. Cristianini, J. Shawe-Taylor, and C. Watkins. Text classification using string kernel. The Journal of Machine Learning Research, 2:419–444, 2001.
[11]Michael L. Mauldin. Chatterbots, tinymuds, and the turing test: Entering the loebner prize competition. In AAAI, 1994.
[12]Ayse Pinar Saygin, Ilyas Cicekli, and Varol Akman. Turing test: 50 years later. Minds and Machines, 10(4):463–518, 2000.
[13]Bhupendra Singh and Upasna Singh. A forensic in- sight into windows 10 cortana search. Computers and Security, 66:142–154, 2017.
[14]Park S. C. Song, W. A novel document clustering model based on latent semantic analysis. In in Proceedings of the Semantics, knowledge and grid, third international conference, pages 539–542, 2007.
[15]D. R. Vukovic and I. M. Dujlovic. Facebook messenger bots and their application for business. In 2016 24th Telecommunications Forum (TELFOR), pages 1–4, 2016.
[16]Richard S. Wallace. The Anatomy of A.L.I.C.E., pages 181–210. Springer Netherlands, 2009.
[17]Joseph Weizenbaum. Eliza — a computer program for the study of natural language communication between man and machine. Common. ACM, 26(1):23–28, January 1983.
[18]E.J. Yannakoudakis, I. Tsomokos, and P.J. Hutton. N- grams and their implication to natural language understanding. Pattern Recognition, 23(5):509 – 528, 1990.