Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

Neural network techniques for NLP tasks

Author : Sonam Gandotra 1 Bhavna Arora 2

Date of Publication :31st October 2017

Abstract: Increase in the number of Internet users have also make people from different communities to interact with each other and hence, a need to provide resources for communicating with each other has given rise to the idea of Natural Language Processing(NLP).NLP is a combination of computer science, artificial intelligence and computational linguistics. In this paper, various NLP tasks are discussed. This paper also describes the various neural net models and their classification on the basis of their architecture and transfer of information from input to output layers via hidden layers. A brief comparison of popular NLP tasks using neural architectures is also done.

Reference :

  1. 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.”


Recent Article