natural-language-processing-3

Arabic Text Diacritization

3260 papers • 126 benchmarks • 313 datasets

Addition of diacritics for undiacritized arabic texts for words disambiguation.

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Arabic Text Diacritization

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Most implemented papers

Arabic Text Diacritization Using Deep Neural Networks

A. Fadel, Ibraheem Tuffaha, Bara' Al-Jawarneh, Mahmoud Al-Ayyoub•Wed Apr 24 2019

The results of the experiments show that the neural Shakkala system significantly outperforms traditional rule-based approaches and other closed-source tools with a Diacritic Error Rate (DER) of 2.88% compared with 13.78%, which the best DER for the non-neural approach is obtained by the Mishkal tool.

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Neural Arabic Text Diacritization: State of the Art Results and a Novel Approach for Machine Translation

A. Fadel, Ibraheem Tuffaha, Bara' Al-Jawarneh, Mahmoud Al-Ayyoub•Thu Oct 31 2019

It is shown that diacritics in Arabic can be used to enhance the models of NLP tasks such as Machine Translation (MT) by proposing the Translation over Diacritization (ToD) approach.

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Multi-components System for Automatic Arabic Diacritization

Hamza Abbad, Shengwu Xiong•Mon Mar 16 2020

An approach to tackle the problem of the automatic restoration of Arabic diacritics that includes three components stacked in a pipeline: a deep learning model which is a multi-layer recurrent neural network with LSTM and Dense layers, a character-level rule-based corrector which applies deterministic operations to prevent some errors, and a word-level statistical corrector that uses the context and the distance information to fix some diacritical issues.

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CAMeL Tools: An Open Source Python Toolkit for Arabic Natural Language Processing

Nizar Habash, Salam Khalifa, Ossama Obeid, Nasser Zalmout, Dima Taji, Mai Oudah, Bashar Alhafni, Go Inoue, Fadhl Eryani, Alexander Erdmann•Thu Apr 30 2020

The design of CAMeL Tools is described and the functionalities it provides are described, including utilities for pre-processing, morphological modeling, Dialect Identification, Named Entity Recognition and Sentiment Analysis.

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Deep Diacritization: Efficient Hierarchical Recurrence for Improved Arabic Diacritization

Badr AlKhamissi, Muhammad N. ElNokrashy, Mohamed Gabr•Sat Oct 31 2020

A novel architecture for labelling character sequences that achieves state-of-the-art results on the Tashkeela Arabic diacritization benchmark using a two-level recurrence hierarchy that operates on the word and character levels separately, enabling faster training and inference than comparable traditional models.

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Effective Deep Learning Models for Automatic Diacritization of Arabic Text

M. Madhfar, Ali Mustafa Qamar•Thu Dec 31 2020

Three deep learning models to recover Arabic text diacritics are proposed based on work in a text-to-speech synthesis system using deep learning, which achieves state-of-the-art performances in both word error rate and diacritic error rate metrics.

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