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<p>Text classification performance can be constrained by single-view approaches that process documents through a single representational lens and struggle to capture the multi-dimensional nature of textual information. We propose FMV-RNN-AE (Feature integration Multi-View RNN Autoencoder), an end-to-end framework that systematically integrates four complementary textual views—word-level embeddings, sentence-level representations, emotion-based features, and paragraph-level semantics. FMV-RNN-AE employs standard RNN autoencoders to learn compressed view-specific representations, followed by a learnable fusion module and joint optimization for classification, focusing on the principled integration of these components rather than introducing a fundamentally new architecture. Comprehensive evaluation across seven benchmark datasets shows consistent improvements of 4.7% compared to strong single-view approaches and 2.2–4.0% over existing multi-view methods, with particularly strong performance on sentiment-oriented tasks (93.5% accuracy on Hate Speech, 92.7% on IMDb). Compared with BERT, FMV-RNN-AE achieves comparable average accuracy while using 7.2<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\times\)</EquationSource> </InlineEquation> fewer parameters and 50% less memory, at the cost of approximately 4<InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\times\)</EquationSource> </InlineEquation> higher inference latency due to sequential multi-view processing. Thus, the framework is best interpreted as a memory-efficient, task-sensitive alternative suited to latency-tolerant, batch or offline scenarios rather than real-time applications. These results highlight the potential of carefully designed multi-view autoencoder integration for improving text classification robustness across diverse domains under constrained memory budgets.</p>

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Multi-view text classification through integrated RNN autoencoder learning of word, sentence, emotion and paragraph representations

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月12日

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  • Yitao Ding,
  • Mohamed Shalaby,
  • Narinderjit Singh Sawaran Singh,
  • Raed H. C. Alfilh

摘要

Text classification performance can be constrained by single-view approaches that process documents through a single representational lens and struggle to capture the multi-dimensional nature of textual information. We propose FMV-RNN-AE (Feature integration Multi-View RNN Autoencoder), an end-to-end framework that systematically integrates four complementary textual views—word-level embeddings, sentence-level representations, emotion-based features, and paragraph-level semantics. FMV-RNN-AE employs standard RNN autoencoders to learn compressed view-specific representations, followed by a learnable fusion module and joint optimization for classification, focusing on the principled integration of these components rather than introducing a fundamentally new architecture. Comprehensive evaluation across seven benchmark datasets shows consistent improvements of 4.7% compared to strong single-view approaches and 2.2–4.0% over existing multi-view methods, with particularly strong performance on sentiment-oriented tasks (93.5% accuracy on Hate Speech, 92.7% on IMDb). Compared with BERT, FMV-RNN-AE achieves comparable average accuracy while using 7.2 \(\times\) fewer parameters and 50% less memory, at the cost of approximately 4 \(\times\) higher inference latency due to sequential multi-view processing. Thus, the framework is best interpreted as a memory-efficient, task-sensitive alternative suited to latency-tolerant, batch or offline scenarios rather than real-time applications. These results highlight the potential of carefully designed multi-view autoencoder integration for improving text classification robustness across diverse domains under constrained memory budgets.