Literature survey of statistical, deep and reinforcement learning in natural language processing

This paper underlines the necessity to incorporate Deep learning and Neural networking in language models under scrutiny for Natural Language Processing. The paper describes various statistical models proposed and the limitations incurred in the same due to limited intelligence of a machine. We have...

Full description

Saved in:
Bibliographic Details
Published in2017 International Conference on Computing, Communication and Automation (ICCCA) pp. 350 - 354
Main Authors Sharma, Akanksha Rai, Kaushik, Pranav
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2017
Subjects
Data mining
Deep Learning
Hidden Markov models
Information Extraction
Information retrieval
Learning (artificial intelligence)
Machine learning
Natural language processing
Neural networks
Reinforcement Learning
Statistical models
TensorFlow
Online AccessGet full text

Cover

More Information
Summary:This paper underlines the necessity to incorporate Deep learning and Neural networking in language models under scrutiny for Natural Language Processing. The paper describes various statistical models proposed and the limitations incurred in the same due to limited intelligence of a machine. We have discussed different neural networks highlighting the importance of Convolutional Neural Networking. We have discussed about open source software TensorFlow that works on Deep learning and the edge it has over the conventional models. Also we have recommended Reinforcement learning as an extension to neural networking which is widely used in gaming for the purpose of Natural Language Processing. We can utilize the reward-driven algorithm for better results.
DOI:10.1109/CCAA.2017.8229841