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> Natural Language Processing

Natural Language Processing A Machine Learning Perspective


Yue Zhang, Westlake University, Zhiyang Teng, Westlake University
Published 2020


With a machine learning approach and less focus on linguistic details, this gentle introduction to natural language processing develops fundamental mathematical and deep learning models for NLP under a unified framework. NLP problems are systematically organised by their machine learning nature, including classification, sequence labelling, and sequence-to-sequence problems. Topics covered include statistical machine learning and deep learning models, text classification and structured prediction models, generative and discriminative models, supervised and unsupervised learning with latent variables, neural networks, and transition-based methods.…

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Key features

  • Systematically discusses natural language processing from a machine learning perspective, delivering a deeper mathematical understanding of NLP solutions. Students can then harness this knowledge to solve NLP tasks and build better NLP models.
  • Provides running examples, figures, and high-level description throughout, allowing students to absorb machine learning concepts and proofs in a meaningful way
  • 200 end-of-chapter questions and 80 illustrations provided throughout, reinforce student understanding
  • Features in-depth discussion of deep learning methods and NLP
  • Establishes a strong correlation between deep learning and linear models for NLP, smoothing the steep learning curve for students as they draw connections between these concepts in a unified framework
  • Explains the reasoning behind NLP models so that engineers will be able to better use, tailor, and even improve them

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