Enhancing Sindhi Word Segmentation using Subword Representation Learning and Position-aware Self-attention

• URL: http://arxiv.org/abs/2012.15079v2
• Date: Wed, 4 Sep 2024 09:44:38 GMT
• Title: Enhancing Sindhi Word Segmentation using Subword Representation Learning and Position-aware Self-attention
• Authors: Wazir Ali, Jay Kumar, Saifullah Tumrani, Redhwan Nour, Adeeb Noor, Zenglin Xu,
• Abstract summary: Sindhi word segmentation is a challenging task due to space omission and insertion issues. Existing Sindhi word segmentation methods rely on designing and combining hand-crafted features. We propose a Subword-Guided Neural Word Segmenter (SGNWS) that addresses word segmentation as a sequence labeling task.
• Score: 19.520840812910357
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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