Enhancing Sindhi word segmentation using subword representation learning and position-aware self-attention
Sindhi word segmentation is a challenging task due to space omission and insertion issues. The Sindhi language itself adds to this complexity. It’s cursive and consists of characters with inherent joining and non-joining properties, independent of word boundaries. Existing Sindhi word segmentation m...
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| Main Authors: | , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
2025
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| In: |
IEEE access
Year: 2025, Volume: 13, Pages: 183133-183142 |
| ISSN: | 2169-3536 |
| DOI: | 10.1109/ACCESS.2024.3507382 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.1109/ACCESS.2024.3507382 Verlag, kostenfrei, Volltext: https://ieeexplore.ieee.org/document/10769409/authors 
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| Author Notes: | Wazir Ali, Jay Kumar, Saifullah Tumrani, Redhwan Nour, Adeeb Noor, and Zenglin Xu (Senior Member, IEEE) |
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| 245 | 1 | 0 | |a Enhancing Sindhi word segmentation using subword representation learning and position-aware self-attention |c Wazir Ali, Jay Kumar, Saifullah Tumrani, Redhwan Nour, Adeeb Noor, and Zenglin Xu (Senior Member, IEEE) |
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| 520 | |a Sindhi word segmentation is a challenging task due to space omission and insertion issues. The Sindhi language itself adds to this complexity. It’s cursive and consists of characters with inherent joining and non-joining properties, independent of word boundaries. Existing Sindhi word segmentation methods rely on designing and combining hand-crafted features. However, these methods have limitations, such as difficulty handling out-of-vocabulary words, limited robustness for other languages, and inefficiency with large amounts of noisy or raw text. Neural network-based models, in contrast, can automatically capture word boundary information without requiring prior knowledge. In this paper, we propose a Subword-Guided Neural Word Segmenter (SGNWS) that addresses word segmentation as a sequence labeling task. The SGNWS model incorporates subword representation learning through a bidirectional long short-term memory encoder, position-aware self-attention, and a conditional random field. Our empirical results demonstrate that the SGNWS model achieves state-of-the-art performance in Sindhi word segmentation on six datasets. | ||
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| 700 | 1 | |a Noor, Adeeb |e VerfasserIn |4 aut | |
| 700 | 1 | |a Xu, Zenglin |e VerfasserIn |4 aut | |
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