KAConvText: Novel Approach to Burmese Sentence Classification using Kolmogorov-Arnold Convolution
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912473009881088 |
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| author | Thu, Ye Kyaw Aung, Thura Oo, Thazin Myint Supnithi, Thepchai |
| author_facet | Thu, Ye Kyaw Aung, Thura Oo, Thazin Myint Supnithi, Thepchai |
| contents | This paper presents the first application of Kolmogorov-Arnold Convolution for Text (KAConvText) in sentence classification, addressing three tasks: imbalanced binary hate speech detection, balanced multiclass news classification, and imbalanced multiclass ethnic language identification. We investigate various embedding configurations, comparing random to fastText embeddings in both static and fine-tuned settings, with embedding dimensions of 100 and 300 using CBOW and Skip-gram models. Baselines include standard CNNs and CNNs augmented with a Kolmogorov-Arnold Network (CNN-KAN). In addition, we investigated KAConvText with different classification heads - MLP and KAN, where using KAN head supports enhanced interpretability. Results show that KAConvText-MLP with fine-tuned fastText embeddings achieves the best performance of 91.23% accuracy (F1-score = 0.9109) for hate speech detection, 92.66% accuracy (F1-score = 0.9267) for news classification, and 99.82% accuracy (F1-score = 0.9982) for language identification. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_06753 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | KAConvText: Novel Approach to Burmese Sentence Classification using Kolmogorov-Arnold Convolution Thu, Ye Kyaw Aung, Thura Oo, Thazin Myint Supnithi, Thepchai Computation and Language Artificial Intelligence I.2.7; I.2.6 This paper presents the first application of Kolmogorov-Arnold Convolution for Text (KAConvText) in sentence classification, addressing three tasks: imbalanced binary hate speech detection, balanced multiclass news classification, and imbalanced multiclass ethnic language identification. We investigate various embedding configurations, comparing random to fastText embeddings in both static and fine-tuned settings, with embedding dimensions of 100 and 300 using CBOW and Skip-gram models. Baselines include standard CNNs and CNNs augmented with a Kolmogorov-Arnold Network (CNN-KAN). In addition, we investigated KAConvText with different classification heads - MLP and KAN, where using KAN head supports enhanced interpretability. Results show that KAConvText-MLP with fine-tuned fastText embeddings achieves the best performance of 91.23% accuracy (F1-score = 0.9109) for hate speech detection, 92.66% accuracy (F1-score = 0.9267) for news classification, and 99.82% accuracy (F1-score = 0.9982) for language identification. |
| title | KAConvText: Novel Approach to Burmese Sentence Classification using Kolmogorov-Arnold Convolution |
| topic | Computation and Language Artificial Intelligence I.2.7; I.2.6 |
| url | https://arxiv.org/abs/2507.06753 |