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A Primer on Neural Network Models for Natural Language Processing

The Journal of artificial intelligence research, , Vol.57, p.345-420 [Peer Reviewed Journal]

2016. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the associated terms available at https://www.jair.org/index.php/jair/about ;ISSN: 1076-9757 ;EISSN: 1943-5037 ;DOI: 10.1613/jair.4992

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  • Title:
    A Primer on Neural Network Models for Natural Language Processing
  • Author: Goldberg, Yoav
  • Subjects: Computation ; Language ; Machine learning ; Natural language processing ; Neural networks ; Object recognition ; Speech processing ; Speech recognition
  • Is Part Of: The Journal of artificial intelligence research, , Vol.57, p.345-420
  • Description: Over the past few years, neural networks have re-emerged as powerful machine-learning models, yielding state-of-the-art results in fields such as image recognition and speech processing. More recently, neural network models started to be applied also to textual natural language signals, again with very promising results. This tutorial surveys neural network models from the perspective of natural language processing research, in an attempt to bring natural-language researchers up to speed with the neural techniques. The tutorial covers input encoding for natural language tasks, feed-forward networks, convolutional networks, recurrent networks and recursive networks, as well as the computation graph abstraction for automatic gradient computation.
  • Publisher: San Francisco: AI Access Foundation
  • Language: English
  • Identifier: ISSN: 1076-9757
    EISSN: 1943-5037
    DOI: 10.1613/jair.4992
  • Source: American Association for Artificial Intelligence (Open Access)
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