M. M. Lopez, “Deep Learning applied to NLP.”

P. Resnik, “Language Log » Four revolutions,” 2011. [Online]. Available: http://languagelog.ldc.upenn.edu/nll/?p=2946. [Accessed: 02-Nov-2017].

[3] N. Hardeniya, J. Perkins, and D. Chopra, “Stemming words - Natural Language Processing: Python and NLTK.” [Online]. Available: https://www.packtpub.com/mapt/book/big_data_and _business_intelligence/9781787285101/12/ch02lvl1 sec019/stemming-words. [Accessed: 02-Nov-2017].

[4] Robin, “Tokenization - Natural Language Processing,” 2009. [Online]. Available: http://language.worldofcomputing.net/category/toke nization. [Accessed: 02-Nov-2017].

[5] L. Speech, Processing, Daniel, H. James, and Martin, “Part-of-Speech Tagging.”

[6] R. Sharnagat, “Named Entity Recognition : A Literature Survey,” pp. 1–27, 2014.

[7] “Sentiment analysis algorithms and applications: A survey,” Ain Shams Eng. J., vol. 5, no. 4, pp. 1093– 1113, Dec. 2014.

[8] M. Gambhir and V. Gupta, “Recent automatic text summarization techniques :” Artif. Intell. Rev., vol. 47, no. 1, pp. 1–66, 2017.

[9] Y. Lecun, Y. Bengio, and G. Hinton, “Deep learning,” 2015.

[10] C. Masolo, “Supervised, unsupervised and deep learning – Towards Data Science – Medium,” 2017. [Online]. Available: https://medium.com/towardsdata-science/supervised-unsupervised-and-deeplearning-aa61a0e5471c. [Accessed: 07-Oct-2017].

[11] M. Riedmiller, “Advanced Supervised Learning in Multi-layer Perceptrons - From Backpropagation to Adaptive Learning Algorithms.”

[12] J. McGonagle, “Feedforward Neural Networks | Brilliant Math & Science Wiki.” [Online]. Available: https://brilliant.org/wiki/feedforwardneural-networks/. [Accessed: 02-Oct-2017].

[13] “A Beginner’s Guide to Recurrent Networks and LSTMs - Deeplearning4j: Open-source, Distributed Deep Learning for the JVM.” [Online]. Available: https://deeplearning4j.org/lstm.html. [Accessed: 03- Oct-2017].

[14] A. Chinea, “Understanding the Principles of Recursive Neural Networks: A Generative Approach to Tackle Model Complexity.”