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Demystifying Text Generation approaches

Lichi Upadhyay1 , M.I. Hasan2 , P.S. Patel3

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-2 , Page no. 788-791, Feb-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i2.788791

Online published on Feb 28, 2019

Copyright © Lichi Upadhyay, M.I. Hasan, P.S. Patel . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Lichi Upadhyay, M.I. Hasan, P.S. Patel, “Demystifying Text Generation approaches,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.788-791, 2019.

MLA Style Citation: Lichi Upadhyay, M.I. Hasan, P.S. Patel "Demystifying Text Generation approaches." International Journal of Computer Sciences and Engineering 7.2 (2019): 788-791.

APA Style Citation: Lichi Upadhyay, M.I. Hasan, P.S. Patel, (2019). Demystifying Text Generation approaches. International Journal of Computer Sciences and Engineering, 7(2), 788-791.

BibTex Style Citation:
@article{Upadhyay_2019,
author = {Lichi Upadhyay, M.I. Hasan, P.S. Patel},
title = {Demystifying Text Generation approaches},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {2 2019},
volume = {7},
Issue = {2},
month = {2},
year = {2019},
issn = {2347-2693},
pages = {788-791},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=3745},
doi = {https://doi.org/10.26438/ijcse/v7i2.788791}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i2.788791}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=3745
TI - Demystifying Text Generation approaches
T2 - International Journal of Computer Sciences and Engineering
AU - Lichi Upadhyay, M.I. Hasan, P.S. Patel
PY - 2019
DA - 2019/02/28
PB - IJCSE, Indore, INDIA
SP - 788-791
IS - 2
VL - 7
SN - 2347-2693
ER -

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Abstract

Natural Language Processing (NLP) is a subfield of Artificial Intelligence that is focused on enabling computers to understand and process human languages, to get computers closer to a human level understanding of language. The main emphasis in the task of text generation is to generate semantically and syntactically sound, coherent and meaning full text. At a high level. the techniques has been to train end to end neural network models consisting of an encoder model to produce a hidden representation of text, followed by a decoder model to generate the target. For the task of text generation, various techniques and models are used. Various algorithms which are used to generate text are discussed in the following subsections. In the field of Text Generation, researcher’s main focus is on Hidden Markov Model(HMM) and Long Short Term Memory (LSTM) units which are used to generate sequential text. This paper also discusses limitations of Hidden Markov Model as well as richness of Long Short Term Memory units.

Key-Words / Index Term

Natural Language Processing,HMM,RNN,ANN,LSTM

References

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