Ibraheem Tuffaha

Ibraheem Tuffaha
Jordan University of Science and Technology | Just · Department of Computer Science

Bachelor of Technology

About

13
Publications
4,146
Reads
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89
Citations
Citations since 2016
13 Research Items
89 Citations
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Introduction
Ibraheem Tuffaha currently works at the Department of Computer Science, Jordan University of Science and Technology. Ibraheem does research in Algorithms, Artificial Intelligence and Artificial Neural Network. Their most recent publication is 'Arabic Text Diacritization Using Deep Neural Networks'.

Publications

Publications (13)
Article
In this work, we present several deep learning models for the automatic diacritization of Arabic text. Our models are built using two main approaches, viz. Feed-Forward Neural Network (FFNN) and Recurrent Neural Network (RNN), with several enhancements such as 100-hot encoding, embeddings, Conditional Random Field (CRF), and Block-Normalized Gradie...
Conference Paper
The emergence of Multi-task learning (MTL) models in recent years has helped push the state of the art in Natural Language Understanding (NLU). We strongly believe that many NLU problems in Arabic are especially poised to reap the benefits of such models. To this end, we propose the Arabic Language Understanding Evaluation Benchmark (ALUE), based o...
Conference Paper
In this work, we provide a Genetic-based algorithm that is used to quickly find a placement for a set of objects within a given layout such that access to these objects is optimized. The given layout describes the free locations of the objects and the object handles and the access is done through a corpus of object requests. The proposed algorithm...
Preprint
Arabic dialect identification is a complex problem for a number of inherent properties of the language itself. In this paper, we present the experiments conducted, and the models developed by our competing team, Mawdoo3 AI, along the way to achieving our winning solution to subtask 1 of the Nuanced Arabic Dialect Identification (NADI) shared task....
Preprint
Full-text available
In this paper, we describe our team's effort on the semantic text question similarity task of NSURL 2019. Our top performing system utilizes several innovative data augmentation techniques to enlarge the training data. Then, it takes ELMo pre-trained contextual embeddings of the data and feeds them into an ON-LSTM network with self-attention. This...
Preprint
In this work, we present several deep learning models for the automatic diacritization of Arabic text. Our models are built using two main approaches, viz. Feed-Forward Neural Network (FFNN) and Recurrent Neural Network (RNN), with several enhancements such as 100-hot encoding, embeddings, Conditional Random Field (CRF) and Block-Normalized Gradien...
Conference Paper
Full-text available
Diacritization of Arabic text is both an interesting and a challenging problem at the same time with various applications ranging from speech synthesis to helping students learning the Arabic language. Like many other tasks or problems in Arabic language processing, the weak efforts invested into this problem and the lack of available (open-source)...
Preprint
Diacritization of Arabic text is both an interesting and a challenging problem at the same time with various applications ranging from speech synthesis to helping students learning the Arabic language. Like many other tasks or problems in Arabic language processing, the weak efforts invested into this problem and the lack of available (open-source)...

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