Papers by Mohammed Attia
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%. |
The Morpho-syntactic Annotation of Animacy for a Dependency Parser (L18-1)
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| Challenge: | Animacy is a feature found in nouns such as 'gender', 'number' and 'case' that improves parser accuracy. |
| Approach: | They propose an annotation scheme and parser results for the animacy feature in Russian and Arabic, morphologically rich languages, using the universal dependency framework. |
| Outcome: | The proposed scheme and parser improve on the animacy feature in Russian and Arabic, and the results show that the feature is more accurate than other features found in nouns, namely, 'gender', , and 'number' |
Multilingual Multi-class Sentiment Classification Using Convolutional Neural Networks (L18-1)
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| Challenge: | a new language-independent model for sentiment analysis is proposed for social media . a sentiment dictionary cannot list all the possible ways people can express their opinions . |
| Approach: | They propose a language-independent model for multi-class sentiment analysis using a neural network architecture. |
| Outcome: | The proposed model does not rely on language-specific features such as ontologies, dictionaries, or morphological or syntactic pre-processing. |