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Deep learning and linguistic representation By Shalom Lappin.

By: Material type: TextTextSeries: Machine learning and pattern recognition series/Chapman and Hall , CRCPublication details: Boca Raton: CRC Press, c2021.Edition: 1Description: i-xiv+147PISBN:
  • 9780367648749
Subject(s): DDC classification:
  • 410.285 LAP-D
Contents:
Chapter 1 Introduction: Deep Learning in Natural Language Processing Chapter 2 Learning Syntactic Structure with Deep Neural Networks Chapter 3 Machine Learning and The Sentence Acceptability Task Chapter 4 Predicting Human Acceptability Judgments in Context Chapter 5 Cognitively Viable Computational Models of Linguistic Knowledge Chapter 6 Conclusions and Future Work
Summary: Key Features: combines an introduction to deep learning in AI and NLP with current research on Deep Neural Networks in computational linguistics. is self-contained and suitable for teaching in computer science, AI, and cognitive science courses; it does not assume extensive technical training in these areas. provides a compact guide to work on state of the art systems that are producing a revolution across a range of difficult natural language tasks.
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Item type Current library Home library Call number Status Date due Barcode
Book Book Dept. of Computational Biology and Bioinformatics Processing Center Dept. of Computational Biology and Bioinformatics 410.285 LAP-D (Browse shelf(Opens below)) Available DCB4122

Chapter 1 Introduction: Deep Learning in Natural Language Processing
Chapter 2 Learning Syntactic Structure with Deep Neural Networks
Chapter 3 Machine Learning and The Sentence Acceptability Task
Chapter 4 Predicting Human Acceptability Judgments in Context
Chapter 5 Cognitively Viable Computational Models of Linguistic Knowledge
Chapter 6 Conclusions and Future Work

Key Features: combines an introduction to deep learning in AI and NLP with current research on Deep Neural Networks in computational linguistics. is self-contained and suitable for teaching in computer science, AI, and cognitive science courses; it does not assume extensive technical training in these areas. provides a compact guide to work on state of the art systems that are producing a revolution across a range of difficult natural language tasks.

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