Natural Language Processing

Natural Language Processing
ePUB
  • eBook:
    Natural Language Processing
  • Author:
    Yue Zhang
  • Edition:
    1 edition
  • Categories:
  • Data:
    February 1, 2021
  • ISBN:
    1108420214
  • ISBN-13:
    9781108420211
  • Language:
    English
  • Pages:
    484 pages
  • Format:
    ePUB

Book Description
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. Rich connections are drawn between concepts throughout the book, equipping students with the tools needed to establish a deep understanding of NLP solutions, adapt existing models, and confidently develop innovative models of their own. Featuring a host of examples, intuition, and end of chapter exercises, plus sample code available as an online resource, this textbook is an invaluable tool for the upper undergraduate and graduate student.

Content

Part I - Basics
1. Introduction
2. Counting Relative Frequencies
3. Feature Vectors
4. Discriminative Linear Classifiers
5. A Perspective from Information Theory
6. Hidden Variables

Part II - Structures
7. Generative Sequence Labelling
8. Discriminative Sequence Labelling
9. Sequence Segmentation
10. Predicting Tree Structures
11. Transition-Based Methods for Structured Prediction
12. Bayesian Network

Part III - Deep Learning
13. Neural Network
14. Representation Learning
15. Neural Structured Prediction
16. Working with Two Texts
17. Pre-training and Transfer Learning
18. Deep Latent Variable Models

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