mlsanityData to 7 Oct 2026

Recent Trends in Deep Learning Based Natural Language Processing

arXiv 1708.02709 ↗First posted 9 Aug 2017cs.CLcited by 262

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Abstract (arXiv)

Deep learning methods employ multiple processing layers to learn hierarchical representations of data and have produced state-of-the-art results in many domains. Recently, a variety of model designs and methods have blossomed in the context of natural language processing (NLP). In this paper, we review significant deep learning related models and methods that have been employed for numerous NLP tasks and provide a walk-through of their evolution. We also summarize, compare and contrast the various models and put forward a detailed understanding of the past, present and future of deep learning in NLP.

Key facts. Cited by 262 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Aug 2017.

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Citing papers per year: 2021: 81; 2022: 71; 2023: 55; 2024: 23; 2025: 25; 2026: 7.

262 papers.

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