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Cognitive difference text classification in online knowledge collaboration based on SA-BiLSTM hybrid model.

Fengjun Liu1, Na Zhao2, Guoqing Zhu3

  • 1School of Health Management, Binzhou Medical University, Yantai, 264003, China. liufengjunmail@163.com.

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Summary

This study introduces a novel SA-BiLSTM model for classifying cognitive difference texts in online collaboration. The model enhances knowledge editing efficiency by accurately identifying semantic features and contextual patterns.

Keywords:
BiLSTMCognitive differencesKnowledge collaborationSelf-attention mechanismText classification

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Computational Linguistics

Background:

  • Online knowledge collaboration generates complex texts reflecting cognitive differences.
  • Identifying these texts is crucial for improving group collaboration efficiency.
  • Extracting semantic features and contextual patterns from such texts is challenging.

Purpose of the Study:

  • To develop a classification system for cognitive difference texts based on conceptual relationships.
  • To propose a hybrid SA-BiLSTM architecture for fine-grained text categorization.
  • To evaluate the proposed model's performance, generalization, and robustness.

Main Methods:

  • Developed a classification system mapping conceptual relationships to cognitive differences.
  • Proposed a hybrid SA-BiLSTM architecture integrating self-attention and bidirectional LSTMs.
  • Conducted systematic experiments using the Baidu Encyclopedia dataset, including ablation studies and comparisons with baseline models (FastText, TextCNN, RNN, BERT, RoBERTa).

Main Results:

  • The SA-BiLSTM model achieved superior classification accuracy compared to conventional approaches.
  • The model effectively mitigated semantic ambiguity in text analysis.
  • Demonstrated enhanced domain adaptation capabilities and robustness in evaluations.

Conclusions:

  • The proposed SA-BiLSTM framework offers a viable solution for analyzing cognitive differences in large-scale knowledge collaboration.
  • Integrating attention mechanisms with sequential modeling provides technical advantages for this task.
  • The findings contribute to improving the efficiency and accuracy of collaborative knowledge editing platforms.