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Updated: Aug 13, 2026
06:45
Automated Joint Space Detection Improves Bone Segmentation Accuracy
Published on: November 28, 2025
Uncertainty-aware joint modeling for Sanskrit compound splitting and segmentation
Irfan Ali1, Liliana Lo Presti1, Marco La Cascia1
1Department of Engineering, University of Palermo, Palermo, Italy.
Frontiers in Artificial Intelligence
|August 12, 2026
Summary
This study introduces a novel Transformer-based model for Sanskrit compound word splitting, improving accuracy in identifying word boundaries and reconstructing segments for natural language processing (NLP) applications.
Area of Science:
- Computational Linguistics
- Natural Language Processing (NLP)
- Digital Humanities
Background:
- Sanskrit compound word splitting is complex due to phonological rules (Sandhi) obscuring word boundaries.
- Multi-split cases require identifying multiple hidden boundaries and reconstructing constituent segments, posing a significant challenge.
Purpose of the Study:
- To develop a unified, end-to-end Transformer-based multi-task architecture for Sanskrit compound word splitting.
- To integrate boundary detection and segmented sequence generation within a single framework for improved accuracy.
Main Methods:
- A shared character-level Transformer encoder processes input, feeding into a BiLSTM-CRF branch for boundary prediction.
- A Transformer decoder autoregressively generates the segmented output, guided by boundary probabilities and uncertainty signals from the CRF.
- Uncertainty-aware coupling mechanisms gate encoder representations and create a global boundary-aware context for robust decoding.
Main Results:
- The proposed model achieved exact-match boundary location accuracy of 84.39% and exact segmentation accuracy of 79.34%.
- Character-level accuracy reached 87.79%, with boundary-level Precision, Recall, and F1 scores of 92.84%, 91.66%, and 92.25%, respectively.
- Consistent improvements over state-of-the-art methods were demonstrated.
Conclusions:
- Uncertainty-aware coupling and structured BiLSTM-CRF supervision enhance segmentation performance and boundary detection accuracy.
- The model offers more accurate morphological analysis, benefiting NLP and digital humanities research.
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