Arabic Text Diacritization
6 papers with code • 1 benchmarks • 1 datasets
Addition of diacritics for undiacritized arabic texts for words disambiguation.
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.
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.
We present CAMeL Tools, a collection of open-source tools for Arabic natural language processing in Python.
We propose a novel architecture for labelling character sequences that achieves state-of-the-art results on the Tashkeela Arabic diacritization benchmark.
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.