Home > Archive > 2020 > Volume 10 Number 1 (Jan. 2020) >
IJMLC 2020 Vol.10(1): 75-80 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2020.10.1.901

Deep Learning Method to Estimate the Focus Time of Paragraph

Zeinab Shahbazi and Yung-Cheol Byun

Abstract—There are a lot of reasons that helps to create a text document that one them is time. Time is an important aspect of texts which shows the value of text and it helps searching for various topics easier. We have different type of documents that some of them has time stamp or temporal documents and some of them are without time stamp which are atemporal documents. In this paper we explain problem of paragraph focus time using convolutional neural network (CNN) and natural language processing (NLP) to show the differences between publication time and focus time of document. We implement paragraph focus time which is explains the time period that document content refers and considered to document creation time. We defined a specific time period for this process that document data is related to that and it contains the publication time and also we evaluate our method on various text documents related to historical events in defined lines.

Index Terms—Paragraph focus time, deep learning, convolutional neural network, temporal information retrieval, natural language processing, word2vec.

Zeinab Shahbazi and Yung-Cheol Byun are with the Dept. of Computer Engineering, Jeju National University, Jeju, South Korea (e-mail: z.shahbazi72@gmail.com, yungcheolbyun@gmail.com).

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Cite: Zeinab Shahbazi and Yung-Cheol Byun, "Deep Learning Method to Estimate the Focus Time of Paragraph," International Journal of Machine Learning and Computing vol. 10, no. 1, pp. 75-80, 2020.

Copyright © 2020 by the authors. 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 (CC BY 4.0).

 

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net


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