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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.
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