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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. We also provide a summary of more than 40. popular datasets widely used for text classification . Finally, we provide a quantitative analysis of the performance of different deep Learning models on popular benchmarks, and discuss future research directions.

system classifies each candidate answer as correct or not. Generative QA is a text generation task since it requires generating answers on the fly. This paper only discusses extractive QA. Naturallanguageinference(NLI). NLI, also known as recognizingtextualentailment (RTE), predicts whether the meaning of one text can be inferred from another.

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  Based, Texts, Classification, Learning, Answers, Deep, Deep learning based text classification

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