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        <title>Does a Hybrid Neural Network based Feature Selection Model Improve Text Classification?</title>
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            <namePart type="given">Pushpak</namePart>
            <namePart type="family">Bhattacharyya</namePart>
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            <namePart type="family">Sharma</namePart>
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            <namePart type="given">Rajeev</namePart>
            <namePart type="family">Sangal</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Does a Hybrid Neural Network based Feature Selection Model Improve Text Classification?
%A Dowlagar, Suman
%A Mamidi, Radhika
%Y Bhattacharyya, Pushpak
%Y Sharma, Dipti Misra
%Y Sangal, Rajeev
%S Proceedings of the 17th International Conference on Natural Language Processing (ICON)
%D 2020
%8 December
%I NLP Association of India (NLPAI)
%C Indian Institute of Technology Patna, Patna, India
%F dowlagar-mamidi-2020-hybrid
%X 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.
%U https://aclanthology.org/2020.icon-main.36
%P 272-280

Markdown (Informal)

[Does a Hybrid Neural Network based Feature Selection Model Improve Text Classification?](https://aclanthology.org/2020.icon-main.36) (Dowlagar & Mamidi, ICON 2020)

• Does a Hybrid Neural Network based Feature Selection Model Improve Text Classification? (Dowlagar & Mamidi, ICON 2020)

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).