WORKING OF ACONTEXT-AWARE CONVERSATIONAL ENTITY
DOI:
https://doi.org/10.22159/ajpcr.2017.v10s1.19638Abstract
Abstract — Introduction of new technologies in to the world is increasing rapidly and in order to assist the users to get equipped with such technologies industries are providing customer care services. Contacting a customer care service is subjective to several overheads of selecting options from a listed set, waiting for the switching between selections and awaiting the support of a customer care executive as the process usually requires a human intervention. Hence, a substitute for a personnel is required by the IT industries in order to automate the communication process in assisting the customers. Chatbots with context aware question-answering capabilities can be viewed as a good solution to such customer-care assistance. Development of a chatbot and the complexities involved in getting it to work effectively is delineated in this paper.
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References
Abdul-Kader SA, Woods J. Survey on chatbot design techniques in speech conversation systems. Int. J. Adv. Comput. Sci. Appl.(IJACSA). 2015 Jul 1;6(7).
Marietto MD, de Aguiar RV, Barbosa GD, Botelho WT, Pimentel E, França RD, da Silva VL. Artificial intelligence markup language: A brief tutorial. arXiv preprint arXiv:1307.3091. 2013 Jul 11.
Chowdhury GG. Natural language processing. Annual review of information science and technology. 2003 Jan 1;37(1):51-89.
Yao K, Zweig G, Peng B. Attention with intention for a neural network conversation model. arXiv preprint arXiv:1510.08565. 2015 Oct 29.
D. Bahdanau, K. Cho, and Y. Bengio., â€Neural machine translation by jointly learning to align and translateâ€. In Proceedings of the International Conference on Learning Representations (ICLR), San Diego, CA, 2015.
Zhou X, Hu B, Chen Q, Tang B, Wang X. Answer sequence learning with neural networks for answer selection in community question answering. arXiv preprint arXiv:1506.06490. 2015 Jun 22.
Taylor A, Marcus M, Santorini B. The Penn treebank: an overview. InTreebanks 2003 (pp. 5-22). Springer Netherlands.
Steven B, Klein E, Loper E. Natural language processing with python. OReilly Media Inc. 2009 Jul.
Zou WY, Socher R, Cer DM, Manning CD. Bilingual Word Embeddings for Phrase-Based Machine Translation. InEMNLP 2013 Oct (pp. 1393-1398).
Bengio Y, Ducharme R, Vincent P, Jauvin C. A neural probabilistic language model. Journal of machine learning research. 2003;3(Feb):1137-55.
Vinyals O, Le Q. A neural conversational model. arXiv preprint arXiv:1506.05869. 2015 Jun 19.
Sutskever I, Vinyals O, Le QV. Sequence to sequence learning with neural networks. InAdvances in neural information processing systems 2014 (pp. 3104-3112).
Graves A. Supervised sequence labelling. InSupervised Sequence Labelling with Recurrent Neural Networks 2012 (pp. 5-13). Springer Berlin Heidelberg.
Bird S. NLTK: the natural language toolkit. InProceedings of the COLING/ACL on Interactive presentation sessions 2006 Jul 17 (pp. 69-72). Association for Computational Linguistics.
Lowe R, Pow N, Serban I, Pineau J. The ubuntu dialogue corpus: A large dataset for research in unstructured multi-turn dialogue systems. arXiv preprint arXiv:1506.08909. 2015 Jun 30.
Buck C, Heafield K, Van Ooyen B. N-gram Counts and Language Models from the Common Crawl. InLREC 2014 May (Vol. 2, p. 4).
Sebastien Jean, Kyunghyun Cho, Roland Memisevic and Yoshua Bengio, On Using Very Large Target Vocabulary for Neural Machine Translation, erforarXiv:1412.2007v2, 18 Mar 2015.
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