Fadi Zaraket


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DAVE: Differential Diagnostic Analysis Automation and Visualization from Clinical Notes
Hadi Hamoud | Fadi Zaraket | Chadi Abou Chakra | Mira Dankar
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations

The Differential Analysis Visualizer for Electronic Medical Records (DAVE) is a tool that utilizes natural language processing and machine learning to help visualize diagnostic algorithms in real-time to help support medical professionals in their clinical decision-making process

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Nâbra: Syrian Arabic Dialects with Morphological Annotations
Amal Nayouf | Tymaa Hammouda | Mustafa Jarrar | Fadi Zaraket | Mohamad-Bassam Kurdy
Proceedings of ArabicNLP 2023

This paper presents Nâbra (نَبْرَة), a corpora of Syrian Arabic dialects with morphological annotations. A team of Syrian natives collected more than 6K sentences containing about 60K words from several sources including social media posts, scripts of movies and series, lyrics of songs and local proverbs to build Nâbra. Nâbra covers several local Syrian dialects including those of Aleppo, Damascus, Deir-ezzur, Hama, Homs, Huran, Latakia, Mardin, Raqqah, and Suwayda. A team of nine annotators annotated the 60K tokens with full morphological annotations across sentence contexts. We trained the annotators to follow methodological annotation guidelines to ensure unique morpheme annotations, and normalized the annotations. F1 and 𝜅 agreement scores ranged between 74% and 98% across features, showing the excellent quality of Nâbra annotations. Our corpora are open-source and publicly available as part of the Currasat portal https://sina.birzeit.edu/currasat.

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Arabic Topic Classification in the Generative and AutoML Era
Doha Albared | Hadi Hamoud | Fadi Zaraket
Proceedings of ArabicNLP 2023

Most recent models for Arabic topic classification leveraged fine-tuning existing pre-trained transformer models and targeted a limited number of categories. More recently, advances in automated ML and generative models introduced novel potentials for the task. While these approaches work for English, it is a question of whether they perform well for low-resourced languages; Arabic in particular. This paper presents (i) ArBoNeClass; a novel Arabic dataset with an extended 14-topic class set covering modern books from social sciences and humanities along with newspaper articles, and (ii) a set of topic classifiers built from it. We finetuned an open LLM model to build ArGTClass. We compared its performance against the best models built with Vertex AI (Google), AutoML(H2O), and AutoTrain(HuggingFace). ArGTClass outperformed the VertexAi and AutoML models and was reasonably similar to the AutoTrain model.


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Curras + Baladi: Towards a Levantine Corpus
Karim Al-Haff | Mustafa Jarrar | Tymaa Hammouda | Fadi Zaraket
Proceedings of the Thirteenth Language Resources and Evaluation Conference

This paper presents two-fold contributions: a full revision of the Palestinian morphologically annotated corpus (Curras), and a newly annotated Lebanese corpus (Baladi). Both corpora can be used as a more general Levantine corpus. Baladi consists of around 9.6K morphologically annotated tokens. Each token was manually annotated with several morphological features and using LDC’s SAMA lemmas and tags. The inter-annotator evaluation on most features illustrates 78.5% Kappa and 90.1% F1-Score. Curras was revised by refining all annotations for accuracy, normalization and unification of POS tags, and linking with SAMA lemmas. This revision was also important to ensure that both corpora are compatible and can help to bridge the nuanced linguistic gaps that exist between the two highly mutually intelligible dialects. Both corpora are publicly available through a web portal.


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Arabic Morphological Analyzer with Agglutinative Affix Morphemes and Fusional Concatenation Rules
Fadi Zaraket | Jad Makhlouta
Proceedings of COLING 2012: Demonstration Papers