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Investigating Multi-Level Semantic Extraction with Squash Capsules for Short Text Classification.

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  • 1School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China.

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Summary
This summary is machine-generated.

This study introduces a novel model for short text classification, enhancing feature representation through convolutional networks, a multi-level semantic framework with BiLSTM and attention, and capsule networks. The model significantly improves classification accuracy and robustness on benchmark datasets.

Keywords:
capsule networkdeep learningmulti-level semantic extractionshort text classification

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Area of Science:

  • Natural Language Processing
  • Machine Learning
  • Artificial Intelligence

Background:

  • Short text classification is challenging due to data sparseness and irregularity.
  • Existing methods struggle to effectively capture both global and local features in short texts.

Purpose of the Study:

  • To propose a new classification model that improves short text representation, global feature extraction, and local feature extraction.
  • To enhance classification accuracy and model robustness for short texts.

Main Methods:

  • Utilized convolutional networks for shallow feature extraction from vectorized short texts.
  • Introduced a multi-level semantic extraction framework using BiLSTM, attention mechanism, and normalization.
  • Integrated convolutional features with semantic framework results and employed capsule networks for high-level local information extraction.

Main Results:

  • The proposed model achieved high accuracy on four benchmark datasets: SUBJ (93.8%), TREC (91.94%), MR (82.81%), and ProcCons (98.43%).
  • Demonstrated significant improvements in classification accuracy and model robustness compared to existing approaches.

Conclusions:

  • The novel model effectively addresses the challenges of short text classification by integrating diverse feature extraction techniques.
  • The findings validate the model's superior performance and robustness, offering a promising direction for future research in the field.