Multichannel Variable-Size Convolution for Sentence Classification
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Abstract (arXiv)
We propose MVCNN, a convolution neural network (CNN) architecture for sentence classification. It (i) combines diverse versions of pretrained word embeddings and (ii) extracts features of multigranular phrases with variable-size convolution filters. We also show that pretraining MVCNN is critical for good performance. MVCNN achieves state-of-the-art performance on four tasks: on small-scale binary, small-scale multi-class and largescale Twitter sentiment prediction and on subjectivity classification.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Mar 2016.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 1; 2024: 2.
5 papers.
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- 2024Feature engineering vs. deep learning for paper section identification: Toward applications in Chinese medical literaturecited by 2
- 2024Arabic Text Sentiment Analysis: Reinforcing Human-Performed Surveys with Wider Topic Analysiscited by 1
- 2024Feature engineering vs. deep learning for paper section identification: Toward applications in Chinese medical literature15 Dec 2024
- 2024Arabic Text Sentiment Analysis: Reinforcing Human-Performed Surveys with Wider Topic Analysis4 Mar 2024
- 2023Deep Learning-based Sentiment Classification: A Comparative Survey12 Dec 2023
- 2021Machine Learning (ML)-Centric Resource Management in Cloud Computing: A Review and Future Directions9 May 2021
- 2021Dual Transformer for Point Cloud Analysis27 Apr 2021