Extracting topic related keywords by backtracking CNN based text classifier

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In the last decades, many studies have been done to extract keywords from text and they show remarkable performance. Most of these studies use a rule-based methodology. They usually focus on Part of Speech(POS), collocations, co-occurrences and dependency of words. However, considering the topic of text is very important key for extracting keywords. Thus, in this paper, we proposed a keywords extracting method using Convolutional Neural Network(CNN) based text classifier which has sufficient information about the topic of text. Experimental results show that using topic related information in CNN text classifier model can improve the quality of keywords extraction. Also proposed method is advantageous in that it requires only a deep learning model differently from existing methods.

Original languageEnglish
Title of host publicationProceedings - 2018 Joint 10th International Conference on Soft Computing and Intelligent Systems and 19th International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages93-96
Number of pages4
ISBN (Electronic)9781538626337
DOIs
StatePublished - 2 Jul 2018
Externally publishedYes
EventJoint 10th International Conference on Soft Computing and Intelligent Systems and 19th International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2018 - Toyama, Japan
Duration: 5 Dec 20188 Dec 2018

Publication series

NameProceedings - 2018 Joint 10th International Conference on Soft Computing and Intelligent Systems and 19th International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2018

Conference

ConferenceJoint 10th International Conference on Soft Computing and Intelligent Systems and 19th International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2018
Country/TerritoryJapan
CityToyama
Period5/12/188/12/18

Keywords

  • CNN
  • Extraction
  • Grad-Cam

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