View in EDS HTML Full Text PDF Full Text

Paraphrase identification using collaborative adversarial networks.

Saved in:
Bibliographic Details
Title: Paraphrase identification using collaborative adversarial networks.
Authors: Alzubi, Jafar A.1 (AUTHOR) j.zubi@bau.edu.jo, Jain, Rachna2 (AUTHOR), Kathuria, Abhishek2 (AUTHOR), Khandelwal, Anjali2 (AUTHOR), Saxena, Anmol2 (AUTHOR), Singh, Anubhav2 (AUTHOR)
Source: Journal of Intelligent & Fuzzy Systems. 2020, Vol. 39 Issue 1, p1021-1032. 12p.
Subjects: Paraphrase, Information commons, Identification, Feature extraction
Abstract: The paper presents a Collaborative Adversarial Network (CAN) model for paraphrase identification, which is a collaborative network holding generator that is pitted against an adversarial network called discriminator. There has been tremendous research work and countless examinations done on sentence similarity demonstration. Learning and identifying the constant highlights, specifically in various areas and domains is the main focus of paraphrase identification. It Involves the capture of regular highlights between two sentences and the community-oriented learning upon traditional ill-disposed and adversarial learning for common feature extraction. The model outperforms the MaLSTM model, which is the baseline model, and also proves to be comparable to many of the state-of-the-art techniques. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Intelligent & Fuzzy Systems is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: egs
DbLabel: Engineering Source
An: 144656412
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Paraphrase identification using collaborative adversarial networks.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Alzubi%2C+Jafar+A%2E%22">Alzubi, Jafar A.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> j.zubi@bau.edu.jo</i><br /><searchLink fieldCode="AR" term="%22Jain%2C+Rachna%22">Jain, Rachna</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kathuria%2C+Abhishek%22">Kathuria, Abhishek</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Khandelwal%2C+Anjali%22">Khandelwal, Anjali</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Saxena%2C+Anmol%22">Saxena, Anmol</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Singh%2C+Anubhav%22">Singh, Anubhav</searchLink><relatesTo>2</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Journal+of+Intelligent+%26+Fuzzy+Systems%22">Journal of Intelligent & Fuzzy Systems</searchLink>. 2020, Vol. 39 Issue 1, p1021-1032. 12p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Paraphrase%22">Paraphrase</searchLink><br /><searchLink fieldCode="DE" term="%22Information+commons%22">Information commons</searchLink><br /><searchLink fieldCode="DE" term="%22Identification%22">Identification</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The paper presents a Collaborative Adversarial Network (CAN) model for paraphrase identification, which is a collaborative network holding generator that is pitted against an adversarial network called discriminator. There has been tremendous research work and countless examinations done on sentence similarity demonstration. Learning and identifying the constant highlights, specifically in various areas and domains is the main focus of paraphrase identification. It Involves the capture of regular highlights between two sentences and the community-oriented learning upon traditional ill-disposed and adversarial learning for common feature extraction. The model outperforms the MaLSTM model, which is the baseline model, and also proves to be comparable to many of the state-of-the-art techniques. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Intelligent & Fuzzy Systems is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=144656412
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3233/JIFS-191933
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 12
        StartPage: 1021
    Subjects:
      – SubjectFull: Paraphrase
        Type: general
      – SubjectFull: Information commons
        Type: general
      – SubjectFull: Identification
        Type: general
      – SubjectFull: Feature extraction
        Type: general
    Titles:
      – TitleFull: Paraphrase identification using collaborative adversarial networks.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Alzubi, Jafar A.
      – PersonEntity:
          Name:
            NameFull: Jain, Rachna
      – PersonEntity:
          Name:
            NameFull: Kathuria, Abhishek
      – PersonEntity:
          Name:
            NameFull: Khandelwal, Anjali
      – PersonEntity:
          Name:
            NameFull: Saxena, Anmol
      – PersonEntity:
          Name:
            NameFull: Singh, Anubhav
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 07
              Text: 2020
              Type: published
              Y: 2020
          Identifiers:
            – Type: issn-print
              Value: 10641246
          Numbering:
            – Type: volume
              Value: 39
            – Type: issue
              Value: 1
          Titles:
            – TitleFull: Journal of Intelligent & Fuzzy Systems
              Type: main
ResultId 1