Automatic Text Generation in Macedonian Using Recurrent Neural Networks

2019 ◽  
pp. 1-12
Author(s):  
Ivona Milanova ◽  
Ksenija Sarvanoska ◽  
Viktor Srbinoski ◽  
Hristijan Gjoreski
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Text Generation ◽  
Automatic Text

scholarly journals From Imitation to Prediction, Data Compression vs Recurrent Neural Networks for Natural Language Processing

2018 ◽  
Vol 21 (61) ◽  
pp. 30 ◽  
Author(s):  
Juan Andres Laura ◽  
Gabriel Omar Masi ◽  
Luis Argerich
Keyword(s):  
Neural Networks ◽  
Natural Language Processing ◽  
Natural Language ◽  
Data Compression ◽  
Language Processing ◽  
Recurrent Neural Networks ◽  
Compression Algorithm ◽  
Text Generation ◽  
Generative Processes ◽  
Automatic Text

In recent studies Recurrent Neural Networks were used for generative processes and their surprising performance can be explained by their ability to create good predictions. In addition, Data Compression is also based on prediction. What the problem comes down to is whether a data compressor could be used to perform as well as recurrent neural networks in the natural language processing tasks of sentiment analysis and automatic text generation. If this is possible, then the problem comes down to determining if a compression algorithm is even more intelligent than a neural network in such tasks. In our journey, a fundamental difference between a Data Compression Algorithm and Recurrent Neural Networks has been discovered.

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>

LatticeRnn: Recurrent Neural Networks Over Lattices

2016 ◽  
Author(s):  
Faisal Ladhak ◽  
Ankur Gandhe ◽  
Markus Dreyer ◽  
Lambert Mathias ◽  
Ariya Rastrow ◽  
...  
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks
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