Yue Zhang is Professor at the School of Engineering at Westlake University and Fellow of the Association for Computational Linguistics. He received his Ph.D. from the University of Oxford, and worked as a postdoctoral research associate at the University of Cambridge. He has served as PC chair for EMNLP 2022, Test-of-Time Committee chair for ACL 2024 and 2025, and editor for multiple journals. Zhiyang Teng is a researcher at ByteDance. He has worked at TikTok since 2023. Previously, he was Research Assistant Professor at NTU Singapore and a research fellow at Westlake University. He received his Ph.D. from the Singapore University of Technology and Design in 2018. His research focuses on large language modelling and multimodal reasoning.

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• Preface; Notation; Part I. Basics: 1. Introduction; 2. Data and model; 3. Counting relative frequencies; 4. Feature vector representation and discriminative text classification; 5. Neuron; 6. Information, entropy, and word representation; 7. Latent variables and EM; Part II. Structures: 8. Generative sequence labelling; 9. Discriminative sequence labelling; 10. Sequence segmentation; 11. Predicting tree structures; 12. Transition-based methods for structured prediction; 13. Bayesian network; Part III. Deep Learning: 14. A paradigm shift to neural network; 15. Sequence representation; 16. Neural structured prediction; 17. Representing structures; 18. Sequence-to-sequence models; 19. Transformer pre-training; 20. Deep latent variable models; 21. Language models as competent generalists; 22. Large language models and beyond; Bibliography; Index.

Yue Zhang is Professor at the School of Engineering at Westlake University and Fellow of the Association for Computational Linguistics. He received his Ph.D. from the University of Oxford, and worked as a postdoctoral research associate at the University of Cambridge. He has served as PC chair for EMNLP 2022, Test-of-Time Committee chair for ACL 2024 and 2025, and editor for multiple journals. Zhiyang Teng is a researcher at ByteDance. He has worked at TikTok since 2023. Previously, he was Research Assistant Professor at NTU Singapore and a research fellow at Westlake University. He received his Ph.D. from the Singapore University of Technology and Design in 2018. His research focuses on large language modelling and multimodal reasoning.

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• Preface; Notation; Part I. Basics: 1. Introduction; 2. Data and model; 3. Counting relative frequencies; 4. Feature vector representation and discriminative text classification; 5. Neuron; 6. Information, entropy, and word representation; 7. Latent variables and EM; Part II. Structures: 8. Generative sequence labelling; 9. Discriminative sequence labelling; 10. Sequence segmentation; 11. Predicting tree structures; 12. Transition-based methods for structured prediction; 13. Bayesian network; Part III. Deep Learning: 14. A paradigm shift to neural network; 15. Sequence representation; 16. Neural structured prediction; 17. Representing structures; 18. Sequence-to-sequence models; 19. Transformer pre-training; 20. Deep latent variable models; 21. Language models as competent generalists; 22. Large language models and beyond; Bibliography; Index.

Yue Zhang is Professor at the School of Engineering at Westlake University and Fellow of the Association for Computational Linguistics. He received his Ph.D. from the University of Oxford, and worked as a postdoctoral research associate at the University of Cambridge. He has served as PC chair for EMNLP 2022, Test-of-Time Committee chair for ACL 2024 and 2025, and editor for multiple journals. Zhiyang Teng is a researcher at ByteDance. He has worked at TikTok since 2023. Previously, he was Research Assistant Professor at NTU Singapore and a research fellow at Westlake University. He received his Ph.D. from the Singapore University of Technology and Design in 2018. His research focuses on large language modelling and multimodal reasoning.

Select Guide Rating

• Preface; Notation; Part I. Basics: 1. Introduction; 2. Data and model; 3. Counting relative frequencies; 4. Feature vector representation and discriminative text classification; 5. Neuron; 6. Information, entropy, and word representation; 7. Latent variables and EM; Part II. Structures: 8. Generative sequence labelling; 9. Discriminative sequence labelling; 10. Sequence segmentation; 11. Predicting tree structures; 12. Transition-based methods for structured prediction; 13. Bayesian network; Part III. Deep Learning: 14. A paradigm shift to neural network; 15. Sequence representation; 16. Neural structured prediction; 17. Representing structures; 18. Sequence-to-sequence models; 19. Transformer pre-training; 20. Deep latent variable models; 21. Language models as competent generalists; 22. Large language models and beyond; Bibliography; Index.