This gentle introduction to the most important techniques in natural language processing uses a unified mathematical and algorithmic framework and gradually increases in complexity. Topics covered range from n-gram language models to large language models (LLMs), from perceptron to deep learning, from text classification to structured prediction (e.g., sequence labelling, segmentation, and parsing) and generation, and from discrete representation to neural representation of linguistics structures. This book provides a comprehensive overview of NLP, making it ideal for upper undergraduate and graduate students in computer science and a valuable reference for researchers and engineers. Exercises of varying difficulty are provided as well as teaching slides and tutorial videos. The new edition features three new chapters on pre-trained language models and large language models as well as a new preliminary chapter overviewing data and model as a framework for NLP methods.

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.

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.

This gentle introduction to the most important techniques in natural language processing uses a unified mathematical and algorithmic framework and gradually increases in complexity. Topics covered range from n-gram language models to large language models (LLMs), from perceptron to deep learning, from text classification to structured prediction (e.g., sequence labelling, segmentation, and parsing) and generation, and from discrete representation to neural representation of linguistics structures. This book provides a comprehensive overview of NLP, making it ideal for upper undergraduate and graduate students in computer science and a valuable reference for researchers and engineers. Exercises of varying difficulty are provided as well as teaching slides and tutorial videos. The new edition features three new chapters on pre-trained language models and large language models as well as a new preliminary chapter overviewing data and model as a framework for NLP methods.

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.

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.