View in EDS HTML Full Text PDF Full Text

Regulating the level of manipulation in text augmentation with systematic adjustment and advanced sentence embedding.

Saved in:
Bibliographic Details
Title: Regulating the level of manipulation in text augmentation with systematic adjustment and advanced sentence embedding.
Authors: Cha, Yuho1 (AUTHOR), Lee, Younghoon2 (AUTHOR) yhoon.lee@seoultech.ac.kr
Source: Neural Computing & Applications. Feb2025, Vol. 37 Issue 5, p3473-3487. 15p.
Subjects: Natural language processing, Language research, Noise, Sampling methods
Abstract: Text augmentation, a method for generating samples by applying combinations, noise, and other manipulations to a small dataset, is a crucial technique in natural language processing research. It introduced diversity into the training process, thereby enabling the construction of robust models. The level of manipulation is the most important issue in text augmentation; low-level manipulation generates data similar to the original, resulting in inefficient augmentation because it cannot ensure diversity, whereas high-level manipulation causes reliability issues for labels and degrades the model's performance. Therefore, this paper proposes a systematically adjustable text augmentation technique to address the "level of manipulation" issue. Specifically, it proposes a method for systematically adjusting the data candidate pool for manipulation to provide an appropriate level of randomness during the augmentation process. Furthermore, we propose an advanced sentence embedding methodology to achieve robust pseudo-labeling at the manipulation level. In other words, we leverage combined sentence embedding, which incorporates sentence embedding, document embedding, and XAI information from the original data to assign reliable pseudo-labels. We conducted performance comparisons with existing text augmentation approaches to validate the effectiveness of our proposed methodology. The experimental results demonstrate that the proposed method achieves the highest performance improvement across all the experimental datasets. [ABSTRACT FROM AUTHOR]
Copyright of Neural Computing & Applications is the property of Springer Nature 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: 182883020
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Regulating the level of manipulation in text augmentation with systematic adjustment and advanced sentence embedding.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Cha%2C+Yuho%22">Cha, Yuho</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lee%2C+Younghoon%22">Lee, Younghoon</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> yhoon.lee@seoultech.ac.kr</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Neural+Computing+%26+Applications%22">Neural Computing & Applications</searchLink>. Feb2025, Vol. 37 Issue 5, p3473-3487. 15p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Natural+language+processing%22">Natural language processing</searchLink><br /><searchLink fieldCode="DE" term="%22Language+research%22">Language research</searchLink><br /><searchLink fieldCode="DE" term="%22Noise%22">Noise</searchLink><br /><searchLink fieldCode="DE" term="%22Sampling+methods%22">Sampling methods</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Text augmentation, a method for generating samples by applying combinations, noise, and other manipulations to a small dataset, is a crucial technique in natural language processing research. It introduced diversity into the training process, thereby enabling the construction of robust models. The level of manipulation is the most important issue in text augmentation; low-level manipulation generates data similar to the original, resulting in inefficient augmentation because it cannot ensure diversity, whereas high-level manipulation causes reliability issues for labels and degrades the model's performance. Therefore, this paper proposes a systematically adjustable text augmentation technique to address the "level of manipulation" issue. Specifically, it proposes a method for systematically adjusting the data candidate pool for manipulation to provide an appropriate level of randomness during the augmentation process. Furthermore, we propose an advanced sentence embedding methodology to achieve robust pseudo-labeling at the manipulation level. In other words, we leverage combined sentence embedding, which incorporates sentence embedding, document embedding, and XAI information from the original data to assign reliable pseudo-labels. We conducted performance comparisons with existing text augmentation approaches to validate the effectiveness of our proposed methodology. The experimental results demonstrate that the proposed method achieves the highest performance improvement across all the experimental datasets. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Neural Computing & Applications is the property of Springer Nature 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=182883020
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s00521-024-10663-8
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 15
        StartPage: 3473
    Subjects:
      – SubjectFull: Natural language processing
        Type: general
      – SubjectFull: Language research
        Type: general
      – SubjectFull: Noise
        Type: general
      – SubjectFull: Sampling methods
        Type: general
    Titles:
      – TitleFull: Regulating the level of manipulation in text augmentation with systematic adjustment and advanced sentence embedding.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Cha, Yuho
      – PersonEntity:
          Name:
            NameFull: Lee, Younghoon
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 11
              M: 02
              Text: Feb2025
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 09410643
          Numbering:
            – Type: volume
              Value: 37
            – Type: issue
              Value: 5
          Titles:
            – TitleFull: Neural Computing & Applications
              Type: main
ResultId 1