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This study introduces the Bert-Enhanced text Graph Neural Network (BEGNN) model, which combines structural and semantic information for improved text classification accuracy. The novel approach enhances feature representation by integrating graph neural networks and pre-trained language models.

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Area of Science:

  • Natural Language Processing
  • Machine Learning
  • Artificial Intelligence

Background:

  • Text classification is crucial for assigning tags to text units, with Graph Neural Networks (GNNs) and pre-trained language models showing promise.
  • Existing methods often fail to capture a text unit's inherent structure or overlook vital semantic features.
  • A comprehensive approach is needed to integrate both structural and semantic information for enhanced text processing.

Purpose of the Study:

  • To propose a novel Bert-Enhanced text Graph Neural Network (BEGNN) model for text classification.
  • To effectively leverage both the structural information within text and the semantic features extracted by pre-trained models.
  • To improve text representation by combining and aggregating diverse features.

Main Methods:

  • Constructing a text graph for each text based on word co-occurrence relationships.
  • Utilizing GNNs to extract structural features from the constructed text graphs.
  • Employing BERT (Bidirectional Encoder Representations from Transformers) to extract semantic features.
  • Interacting and aggregating the structural and semantic features of different granularities for a unified representation.

Main Results:

  • The BEGNN model effectively captures both structural and semantic information in text.
  • Experimental results on standard datasets demonstrate the superior performance of the BEGNN model.
  • The proposed method achieves a more effective text representation compared to existing approaches.

Conclusions:

  • The BEGNN model offers a significant advancement in text classification by integrating structural and semantic analysis.
  • This hybrid approach enhances the understanding and representation of textual data.
  • The findings highlight the potential of combining GNNs and pre-trained language models for complex NLP tasks.