Natural Language Processing and Large Language Models: Theory, Hand-on Codes, and Case Studies
Authors: Chengqing Zong, Yang Zhao, Yanjun Ma
Publication date: 24 July 2026 (eBook); 25 July 2026 (hardcover)
Publisher: Springer Singapore
Edition: 1st edition
Length: XIV + 394 pages
Summary
This book provides a broad introduction to Natural Language Processing (NLP) that deliberately combines mathematical and conceptual foundations with executable code and practical case studies. It begins with the building blocks of modern NLP—neural networks, distributed representations, sequence-generation models, and basic language models—and then develops the ideas behind pre-trained language models and large language models. Important architectures and techniques include Transformers, BERT, GPT, ERNIE, and reinforcement learning from human feedback (RLHF). The emphasis is not simply on describing these models, but on explaining how they are trained, adapted, and applied. The authors provide executable Jupyter notebooks and datasets that can be run through Baidu AI Studio.
The second half of the book is strongly application-oriented. Separate chapters cover tokenization and word segmentation, sentiment classification, information extraction, semantic text matching, PEGASUS-based text summarization, intent recognition with ERNIE 3.0, machine reading comprehension, machine translation, and question answering with LLMs. The progression therefore moves from fundamental neural representations to increasingly sophisticated language tasks. In particular, the book shows how modern pre-trained models can be fine-tuned for specific NLP problems rather than treating an LLM as a mysterious black box.
The text is aimed mainly at senior undergraduate and graduate students in computer science or AI, developers, researchers, and newcomers to NLP. Its main strength is the balance between theory and implementation: readers learn both why techniques such as attention, Transformers, BERT and GPT work and how to build systems using them. At nearly 400 pages, it is substantial enough to serve as an NLP course text, while its case-study structure makes it useful for self-study or practical AI projects. It is particularly valuable for someone who wants to progress from classical NLP toward contemporary LLM-based systems without jumping directly into high-level APIs.
Key takeaways
Springer book page and free full text
Authors: Chengqing Zong, Yang Zhao, Yanjun Ma
Publication date: 24 July 2026 (eBook); 25 July 2026 (hardcover)
Publisher: Springer Singapore
Edition: 1st edition
Length: XIV + 394 pages
Quote:The book is available as free pdf access
Summary
This book provides a broad introduction to Natural Language Processing (NLP) that deliberately combines mathematical and conceptual foundations with executable code and practical case studies. It begins with the building blocks of modern NLP—neural networks, distributed representations, sequence-generation models, and basic language models—and then develops the ideas behind pre-trained language models and large language models. Important architectures and techniques include Transformers, BERT, GPT, ERNIE, and reinforcement learning from human feedback (RLHF). The emphasis is not simply on describing these models, but on explaining how they are trained, adapted, and applied. The authors provide executable Jupyter notebooks and datasets that can be run through Baidu AI Studio.
The second half of the book is strongly application-oriented. Separate chapters cover tokenization and word segmentation, sentiment classification, information extraction, semantic text matching, PEGASUS-based text summarization, intent recognition with ERNIE 3.0, machine reading comprehension, machine translation, and question answering with LLMs. The progression therefore moves from fundamental neural representations to increasingly sophisticated language tasks. In particular, the book shows how modern pre-trained models can be fine-tuned for specific NLP problems rather than treating an LLM as a mysterious black box.
The text is aimed mainly at senior undergraduate and graduate students in computer science or AI, developers, researchers, and newcomers to NLP. Its main strength is the balance between theory and implementation: readers learn both why techniques such as attention, Transformers, BERT and GPT work and how to build systems using them. At nearly 400 pages, it is substantial enough to serve as an NLP course text, while its case-study structure makes it useful for self-study or practical AI projects. It is particularly valuable for someone who wants to progress from classical NLP toward contemporary LLM-based systems without jumping directly into high-level APIs.
Key takeaways
- Theory + practice: combines NLP foundations with runnable implementations rather than treating them separately.
- Modern coverage: follows the development from neural networks and embeddings through Transformer → BERT/GPT → modern LLM applications.
- Application-heavy: includes real implementations of summarization, sentiment analysis, information extraction, question answering, machine translation, semantic matching, and related tasks.
- Good learning path: particularly suitable for readers who know some Python/machine learning and want a systematic introduction to NLP and LLM engineering.
- Major advantage: the entire book is Open Access, including downloadable PDF and EPUB editions.
Springer book page and free full text
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