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Deep Learning Based Text Classification: A Comprehensive ...

deep Learning Based Text classification : A Comprehensive Review Shervin Minaee, Snapchat Inc Nal Kalchbrenner, Google Brain, Amsterdam Erik Cambria, Nanyang Technological University, Singapore Narjes Nikzad, University of Tabriz Meysam Chenaghlu, University of Tabriz Jianfeng Gao, Microsoft Research, Redmond [ ] 4 Jan 2021. Abstract. deep Learning Based models have surpassed classical machine Learning Based approaches in various text classification tasks, including sentiment analysis, news categorization, question answering, and natural language inference. In this paper, we provide a Comprehensive review of more than 150 deep Learning Based models for text classification developed in recent years, and discuss their technical contributions, similarities, and strengths.

This section reviews more than 150 DL models proposed for various TC tasks. For clarify, we group these models into several categories based on their model architectures1: •Feed-forward networks view text as a bag of words (Section2.1). •RNN-based models view text as a sequence of words, and are intended to capture word dependencies and

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