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Towards Identifying Objectivity in Short Informal Text.

Chaowei Zhang1, Cheng Zhao1, Zewei Zhang2

  • 1The Department of Information Engineering, Yangzhou University, Yangzhou 225012, China.

Entropy (Basel, Switzerland)
|June 26, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for identifying objectivity in short, informal texts. The approach uses subject-predicate-object (UVO) features to improve objectivity detection in natural language processing (NLP).

Keywords:
large language modelsobjectivity learningrelational triplesshort informal texts

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Area of Science:

  • Natural Language Processing (NLP)
  • Computational Linguistics
  • Artificial Intelligence (AI)

Background:

  • Short informal texts are common but challenging for NLP due to fragmented grammar and limited context.
  • Existing NLP tasks often focus on subjectivity (e.g., sentiment analysis), neglecting objectivity.
  • Objectivity identification is crucial for applications like information filtering and content verification.

Purpose of the Study:

  • To propose a novel task for identifying objectivity in short informal texts.
  • To develop a method that leverages linguistic features for objectivity detection.
  • To address the underexplored area of objectivity learning in informal text.

Main Methods:

  • A two-stage approach for objectivity identification was developed.
  • A UVO (Subject-Predicate-Object) quantification module using OpenIE and LLMs was implemented.
  • An objectivity identification module utilized pre-trained models (BERT, RoBERTa) constrained by UVO features.

Main Results:

  • The proposed approach demonstrated superior performance compared to baseline models.
  • Objective-F1 scores improved by up to 5.91%.
  • Accuracy was enhanced by up to 6.97%.

Conclusions:

  • The UVO quantification method effectively enhances objectivity identification in short informal texts.
  • The proposed approach offers a significant advancement in NLP for discerning objective content.
  • This work opens new avenues for research in objective text analysis.