Transcription of Deep Learning Based Text Classification: A Comprehensive ...
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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.
semantic analysis (LSA) developed by Dumais et al. [1] in 1989. LSA is a linear model with less than 1 million parameters, trained on 200K words. In 2001, Bengio et al. [2] propose the first neural language model based on a feed-forward neural network trained on 14 million words. However, these early embedding models underperform
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