Back-Propagation Neural Network Approach to Myanmar Part-of-Speech Tagging

2016 ◽  
pp. 212-220
Author(s):  
Hay Mar Hnin ◽  
Win Pa Pa ◽  
Ye Kyaw Thu
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Network Approach ◽  
Neural Network Approach ◽  
Part Of Speech Tagging ◽  
Part Of Speech ◽  
Speech Tagging

scholarly journals Part of Speech Tagging: Shallow or Deep Learning?

2018 ◽  
Vol 5 ◽  
pp. 1-15 ◽  
Author(s):  
Robert Östling
Keyword(s):  
Neural Network ◽  
Computational Efficiency ◽  
State Of The Art ◽  
Neural Network Approach ◽  
Part Of Speech Tagging ◽  
Pos Tagging ◽  
Part Of Speech ◽  
The Neural Network ◽  
Efficient Machine ◽  
Speech Tagging

Deep neural networks have advanced the state of the art in numerous fields, but they generally suffer from low computational efficiency and the level of improvement compared to more efficient machine learning models is not always significant. We perform a thorough PoS tagging evaluation on the Universal Dependencies treebanks, pitting a state-of-the-art neural network approach against UDPipe and our sparse structured perceptron-based tagger, efselab. In terms of computational efficiency, efselab is three orders of magnitude faster than the neural network model, while being more accurate than either of the other systems on 47 of 65 treebanks.

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