2017 RecentTrendsinDeepLearningBased

• (Young et al., 2017) ⇒ Tom Young, Devamanyu Hazarika, Soujanya Poria, and Erik Cambria. (2017). “Recent Trends in Deep Learning Based Natural Language Processing.” In: IEEE Computational Intelligence Magazine Journal, 13(3). DOI: 10.1109/MCI.2018.2840738 arXiv:1708.02709

Subject Headings: Deep Learning Neural Network; Neural Natural Language Processing System.

Cited By

• http://scholar.google.com/scholar?q=%222017%22+Recent+Trends+in+Deep+Learning+Based+Natural+Language+Processing

Quotes

Author Keywords

• Natural Language Processing; Deep Learning; Word2Vec; Attention; Recurrent Neural Networks; Convolutional Neural Networks; LSTM; Sentiment Analysis; Question Answering; Dialogue Systems; Parsing; Named-Entity Recognition; POS Tagging; Semantic Role Labeli

Abstract

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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2017 RecentTrendsinDeepLearningBased	Tom Young Devamanyu Hazarika Soujanya Poria Erik Cambria			Recent Trends in Deep Learning Based Natural Language Processing				10.1109/MCI.2018.2840738