Arabic Text Diacritization
6 papers with code • 1 benchmarks • 1 datasets
Addition of diacritics for undiacritized arabic texts for words disambiguation.
Benchmarks
These leaderboards are used to track progress in Arabic Text Diacritization
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Arabic Text Diacritization
Most implemented papers
Arabic Text Diacritization Using Deep Neural Networks
After constructing the dataset, existing tools and systems are tested on it.
Neural Arabic Text Diacritization: State of the Art Results and a Novel Approach for Machine Translation
In this work, we present several deep learning models for the automatic diacritization of Arabic text.
Multi-components System for Automatic Arabic Diacritization
Hamza5/Pipeline-diacritizer
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In this paper, we propose 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 which uses the context and the distance information to fix some diacritization issues.
CAMeL Tools: An Open Source Python Toolkit for Arabic Natural Language Processing
CAMeL-Lab/camel_tools
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LREC 2020
We present CAMeL Tools, a collection of open-source tools for Arabic natural language processing in Python.
Deep Diacritization: Efficient Hierarchical Recurrence for Improved Arabic Diacritization
BKHMSI/deep-diacritization
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COLING (WANLP) 2020
We propose a novel architecture for labelling character sequences that achieves state-of-the-art results on the Tashkeela Arabic diacritization benchmark.
Effective Deep Learning Models for Automatic Diacritization of Arabic Text
almodhfer/Arabic_Diacritization
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We propose three deep learning models to recover Arabic text diacritics based on our work in a text-to-speech synthesis system using deep learning.
