Papers by Randah Alharbi
Part-of-Speech Tagging for Arabic Gulf Dialect Using Bi-LSTM (L18-1)
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| Challenge: | Part-of-speech (POS) tagging is one of the most important building blocks in many natural language processing (NLP) applications. |
| Approach: | They propose to use a POS tagger for Arabic Gulf dialect to improve POS tagging accuracy. |
| Outcome: | The proposed POS tagger improves POS tagging accuracy for the Arabic Gulf dialect from 75% accuracy to 91% accuracy using a bi-LSTM labeler. |
Multi-Dialect Arabic POS Tagging: A CRF Approach (L18-1)
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Kareem Darwish, Hamdy Mubarak, Ahmed Abdelali, Mohamed Eldesouki, Younes Samih, Randah Alharbi, Mohammed Attia, Walid Magdy, Laura Kallmeyer
| Challenge: | Existing work on dialectal POS tagging is rather scant with POS tags for most dialects being nonexistent or of limited availability. |
| Approach: | They propose a dataset of POS-tagged Arabic tweets in four major dialects and a tagging guideline for each dialect. |
| Outcome: | The proposed model can tag four different dialects with an average accuracy of 89.3%. |