Does a Hybrid Neural Network based Feature Selection Model Improve Text Classification?

Suman Dowlagar, Radhika Mamidi

Abstract
Text classification is a fundamental problem in the field of natural language processing. Text classification mainly focuses on giving more importance to all the relevant features that help classify the textual data. Apart from these, the text can have redundant or highly correlated features. These features increase the complexity of the classification algorithm. Thus, many dimensionality reduction methods were proposed with the traditional machine learning classifiers. The use of dimensionality reduction methods with machine learning classifiers has achieved good results. In this paper, we propose a hybrid feature selection method for obtaining relevant features by combining various filter-based feature selection methods and fastText classifier. We then present three ways of implementing a feature selection and neural network pipeline. We observed a reduction in training time when feature selection methods are used along with neural networks. We also observed a slight increase in accuracy on some datasets.
Anthology ID:
2020.icon-main.36
Volume:
Proceedings of the 17th International Conference on Natural Language Processing (ICON)
Month:
December
Year:
2020
Address:
Indian Institute of Technology Patna, Patna, India
Editors:
Pushpak Bhattacharyya, Dipti Misra Sharma, Rajeev Sangal
Venue:
ICON
SIG:
Publisher:
NLP Association of India (NLPAI)
Note:
Pages:
272–280
Language:
URL:
https://aclanthology.org/2020.icon-main.36
DOI:
Bibkey:
Cite (ACL):
Suman Dowlagar and Radhika Mamidi. 2020. Does a Hybrid Neural Network based Feature Selection Model Improve Text Classification?. In Proceedings of the 17th International Conference on Natural Language Processing (ICON), pages 272–280, Indian Institute of Technology Patna, Patna, India. NLP Association of India (NLPAI).
Cite (Informal):
Does a Hybrid Neural Network based Feature Selection Model Improve Text Classification? (Dowlagar & Mamidi, ICON 2020)
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