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The College Chatbot

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Darshil Gada, Yash Mehta, Riddhish Shah, Mohan Sharma, Aayush Shah
10.5120/ijca2017915367

Darshil Gada, Yash Mehta, Riddhish Shah, Mohan Sharma and Aayush Shah. The College Chatbot. International Journal of Computer Applications 173(7):34-37, September 2017. BibTeX

@article{10.5120/ijca2017915367,
	author = {Darshil Gada and Yash Mehta and Riddhish Shah and Mohan Sharma and Aayush Shah},
	title = {The College Chatbot},
	journal = {International Journal of Computer Applications},
	issue_date = {September 2017},
	volume = {173},
	number = {7},
	month = {Sep},
	year = {2017},
	issn = {0975-8887},
	pages = {34-37},
	numpages = {4},
	url = {http://www.ijcaonline.org/archives/volume173/number7/28350-2017915367},
	doi = {10.5120/ijca2017915367},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

A lot of metadata, information about data, is stored in the world that can help us in gathering significant patterns and forming complex relations among data. This information can be utilised carefully to extract some meaningful and marketable products. A very common example of metadata is information about students in a university. This data is used by our system, The College Chatbot (CC), to provide quick and smart replies to queries provided by students as well as teachers. The CC will remove the redundancy of teachers sending information or notifying students of something via a middleware such as Whatsapp or a middle agent i.e the class representative. It will also allow the students to search for their seniors information regarding their future decisions or information related to their colleagues or their teachers. The system also provides an easy access to all the placement information a student requires in a convenient and efficient manner.

References

  1. Julia M Taylor, Toward computational recognition of humorous intent, January 2004 at https://www.researchgate.net/publication/22835385
  2. Hannu Jaakkola and Bernhard Thalheim. (2011), Architecture-driven modelling methodologies, Proceedings of the 2011 conference on Information Modelling and Knowledge Bases XXII . Anneli Heimbürger et al. (eds). IOS Press. p. 98
  3. Paul C. Clements (1996) "A survey of architecture description languages." Proceedings of the 8th international workshop on software specification and design. IEEE Computer Society, 1996.
  4. Bahdanau, D., Cho, K., and Bengio, Y. Neural machine translation by jointly learning to align and translate .arXiv preprint arXiv:1409.0473, 2014.
  5. Abu Shawar B., Atwell E., Different measurement metrics to evaluate a chatbot system, Bridging the Gap: Academic and Industrial Research in Dialog Technologies.
  6. Anish Talwar, Yogesh Kumar, Machine Learning: An artificial intelligence methodology, International Journal Of Engineering And Computer Science ISSN:2319-7242 Volume 2 Issue 12, Dec.2013 Page No. 3400-3404
  7. Zhou Yu, Ziyu Xu, AlanWBlack, Alexander I. Rudnicky, “Chatbot Evaluation and Database Expansion via Crowdsourcing”

Keywords

Intent analysis, Entity analysis, Chatbots