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Integrating techniques of social network analysis and word embedding for word sense disambiguation.

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Title: Integrating techniques of social network analysis and word embedding for word sense disambiguation.
Authors: Hung, Chihli1 (AUTHOR) chihli@cycu.edu.tw, Hung, Chih-Neng2 (AUTHOR) chihneng@mail.cjcu.edu.tw, Chou, Hsien-Ming1 (AUTHOR) chou0109@cycu.edu.tw
Source: Kybernetes. 2026, Vol. 55 Issue 6, p2521-2535. 15p.
Subjects: Social network analysis, Polysemy, Natural language processing, Machine learning, Text mining
Abstract: Purpose: This research addresses the challenge of polysemous words in word embedding techniques, which are commonly used in text mining. It aims to resolve word sense ambiguity by introducing a social network sense disambiguation (SNSD) model based on social network analysis (SNA). Design/methodology/approach: The SNSD model treats words as members of a social network and their co-occurrence relationships as interactions. By analyzing these interactions, the model identifies words with high betweenness centrality, which may act as bridges between different word sense communities, indicating polysemy. This unsupervised method does not rely on pre-tagged resources and is validated using the IMDb dataset. Findings: The SNSD model effectively resolves word sense ambiguity in word embeddings, proving to be a cost-effective and adaptable solution to this issue. The experimental results demonstrate that the model enhances the accuracy of word embeddings by accurately identifying the correct meanings of polysemous words. Originality/value: This study is the first to apply SNA to word sense disambiguation (WSD). The SNSD model offers a novel, unsupervised approach that overcomes the limitations of traditional supervised or knowledge-based methods, providing a valuable contribution to the field of text mining. [ABSTRACT FROM AUTHOR]
Copyright of Kybernetes is the property of Emerald Publishing Limited 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
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Header DbId: egs
DbLabel: Engineering Source
An: 193956366
AccessLevel: 6
PubType: Periodical
PubTypeId: serialPeriodical
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Integrating techniques of social network analysis and word embedding for word sense disambiguation.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Hung%2C+Chihli%22">Hung, Chihli</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> chihli@cycu.edu.tw</i><br /><searchLink fieldCode="AR" term="%22Hung%2C+Chih-Neng%22">Hung, Chih-Neng</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> chihneng@mail.cjcu.edu.tw</i><br /><searchLink fieldCode="AR" term="%22Chou%2C+Hsien-Ming%22">Chou, Hsien-Ming</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> chou0109@cycu.edu.tw</i>
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  Data: <searchLink fieldCode="JN" term="%22Kybernetes%22">Kybernetes</searchLink>. 2026, Vol. 55 Issue 6, p2521-2535. 15p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Social+network+analysis%22">Social network analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Polysemy%22">Polysemy</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+language+processing%22">Natural language processing</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Text+mining%22">Text mining</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Purpose: This research addresses the challenge of polysemous words in word embedding techniques, which are commonly used in text mining. It aims to resolve word sense ambiguity by introducing a social network sense disambiguation (SNSD) model based on social network analysis (SNA). Design/methodology/approach: The SNSD model treats words as members of a social network and their co-occurrence relationships as interactions. By analyzing these interactions, the model identifies words with high betweenness centrality, which may act as bridges between different word sense communities, indicating polysemy. This unsupervised method does not rely on pre-tagged resources and is validated using the IMDb dataset. Findings: The SNSD model effectively resolves word sense ambiguity in word embeddings, proving to be a cost-effective and adaptable solution to this issue. The experimental results demonstrate that the model enhances the accuracy of word embeddings by accurately identifying the correct meanings of polysemous words. Originality/value: This study is the first to apply SNA to word sense disambiguation (WSD). The SNSD model offers a novel, unsupervised approach that overcomes the limitations of traditional supervised or knowledge-based methods, providing a valuable contribution to the field of text mining. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Kybernetes is the property of Emerald Publishing Limited 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.)
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      – Code: eng
        Text: English
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        PageCount: 15
        StartPage: 2521
    Subjects:
      – SubjectFull: Social network analysis
        Type: general
      – SubjectFull: Polysemy
        Type: general
      – SubjectFull: Natural language processing
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Text mining
        Type: general
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      – TitleFull: Integrating techniques of social network analysis and word embedding for word sense disambiguation.
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            NameFull: Hung, Chihli
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            NameFull: Hung, Chih-Neng
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            NameFull: Chou, Hsien-Ming
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            – D: 01
              M: 05
              Text: 2026
              Type: published
              Y: 2026
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