arXiv:2606.24758v1 Announce Type: new Abstract: Handling repeated characters in text can be tricky, since they can represent either the correct spelling of a word or informal character elongation often seen in social media posts. We present CANDLE, a lightweight system for character-level Arabic noise deduplication that addresses this challenge without relying on handcrafted rules, dictionaries, or morphological analyzers. At the heart of CANDLE is a novel application of Connectionist Temporal Classification (CTC) to this task, a formulation not previously explored for character deduplication,
The proliferation of informal text on digital platforms continues to drive the need for robust NLP solutions.
This development offers a technical improvement for handling noisy Arabic text, which can enhance the accuracy of NLP applications.
A specific technical challenge in character-level Arabic noise deduplication now has a new, potentially more efficient, solution.
- · Arabic NLP developers
- · Social media analytics platforms
- · Search engines
- · Developers relying on handcrafted rules for text normalization
Improved accuracy in Arabic text analysis and understanding.
Better performance in downstream NLP tasks such as sentiment analysis or machine translation for Arabic.
Potentially broader adoption of AI tools in Arabic-speaking markets due to enhanced language processing capabilities.
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Read at arXiv cs.CL