Automatic diacritization of Arabic text using recurrent neural networks

2015 ◽  
Vol 18 (2) ◽  
pp. 183-197 ◽  
Author(s):  
Gheith A. Abandah ◽  
Alex Graves ◽  
Balkees Al-Shagoor ◽  
Alaa Arabiyat ◽  
Fuad Jamour ◽  
...  
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Arabic Text

Arabic Text Generation Using Recurrent Neural Networks

2018 ◽  
pp. 523-533 ◽  
Author(s):  
Adnan Souri ◽  
Zakaria El Maazouzi ◽  
Mohammed Al Achhab ◽  
Badr Eddine El Mohajir
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Arabic Text ◽  
Text Generation

Levenshtein Augmentation Improves Performance of SMILES Based Deep-Learning Synthesis Prediction

2020 ◽  
Author(s):  
Dean Sumner ◽  
Jiazhen He ◽  
Amol Thakkar ◽  
Ola Engkvist ◽  
Esben Jannik Bjerrum
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Recurrent Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Sequence Similarity ◽  
Learning Models ◽  
Underlying Network

<p>SMILES randomization, a form of data augmentation, has previously been shown to increase the performance of deep learning models compared to non-augmented baselines. Here, we propose a novel data augmentation method we call “Levenshtein augmentation” which considers local SMILES sub-sequence similarity between reactants and their respective products when creating training pairs. The performance of Levenshtein augmentation was tested using two state of the art models - transformer and sequence-to-sequence based recurrent neural networks with attention. Levenshtein augmentation demonstrated an increase performance over non-augmented, and conventionally SMILES randomization augmented data when used for training of baseline models. Furthermore, Levenshtein augmentation seemingly results in what we define as <i>attentional gain </i>– an enhancement in the pattern recognition capabilities of the underlying network to molecular motifs.</p>

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