Deep Learning for Natural Language Processing

By Palash Goyal, Sumit Pandey, K. Jain. Apress, 2019

555 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/4199089b239c6d38e5051208210cb66454bdbba0

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Citations in the corpus

A Survey of Evaluation Metrics Used for NLG Systems (2020, background): “The second major revolution was the introduction of deep neural network based models which were able to learn from large amounts of data and establish new state of the art results on a wide variety of tasks [32, 178].”
Information Retrieval: A Biomedical and Health Perspective (2020, background): “6, the CrossRef standard allows linking of clinical trials publications with their data [229].”
Collaborative Multi-Agent Dialogue Model Training Via Reinforcement Learning (2019, background): “…al., 2013; Oh and Rudnicky, 2000; Zen et al., 2009; Reiter and Dale, 2000; Rieser and Lemon, 2010), especially when adopting deep learning models (Deng and Liu, 2018; Mesnil et al., 2015; Wen et al., 2015, 2017; Su et al., 2017; Papangelis et al., 2018; Liu and Lane, 2018b; Li et al., 2017;…”
Deep Reinforcement Learning: An Overview (2017, background): “…(Russell and Norvig, 2009), deep learning in neural networks (Schmidhuber, 2015); natural language processing (NLP) (Hirschberg and Manning, 2015; Deng and Liu, 2017); robotics (Kober et al., 2013); transfer learning (Taylor and Stone, 2009; Pan and Yang, 2010; Weiss et al., 2016);…”
Federated Learning in Edge Computing: A Systematic Survey (2022, background): “…including Computer Vision (CV) [36,37], healthcare [38], game playing [39,40], autonomous vehicles [41,42], and Natural Language Processing (NLP) [43,44] without the costly hand-crafted feature engineering required in conventional ML. DL is a subﬁeld of ML concerned with algorithms inspired by…”
State-of-the-art review on deep learning in medical imaging. (2019, background): “The most immediate effect can be felt in the field of image processing (2), robotics (3), computer games (4), natural language processing (5), self-driving cars (6) and many others.”
Speech Recognition Using Deep Neural Networks: A Systematic Review (2019, background): “Their final results clearly showed that the word error rate (WER) reduced on four major benchmarks by 30% compared to the state-of-the-art models based on Gaussian mixtures [2].”
Adversarial Attacks on Deep Learning Models in Natural Language Processing: A Survey (2019, background): “It was initially developed to overcome the difficulty of encoding a long sequence required in Seq2Seq models [77].”
A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends (2019, background): “NLP [263] Inflectional Review of Deep Learning on Natural Language Processing [264] Deep learning for natural language processing: advantages and challenges [265] Deep Learning for Natural Language Processing [266]”