Natural Language Processing

Neural Networks and Large Language Models
Erscheinungsjahr 2027
ISBN: 978-981-9265-98-5
Verlag: Springer

Buch, Englisch, Format (B × H): 155 mm x 235 mm

This book offers a comprehensive and forward-looking introduction to Natural Language Processing (NLP). Moving beyond traditional NLP approaches, this book presents a unified framework that connects foundational methods with the breakthroughs enabled by Large Language Models (LLMs).

Designed for students, engineers, and researchers in computer science and artificial intelligence, this volume bridges the gap between classic NLP concepts and modern deep learning paradigms. Part I introduces the core principles of machine learning and neural networks, building the foundation for understanding representation learning. Part II examines key neural architectures—word vectors, recurrent and convolutional models, sequence-to-sequence systems, and the Transformer—that have revolutionized NLP in the past decade. Part III brings readers to the cutting edge, covering pre-training, generative modeling, prompt design, alignment, and inference in LLMs.

By blending theory with practical insight, this book helps readers grasp both how and why neural NLP works. It answers essential questions: What makes LLMs fundamentally different from earlier models? How do they generate, align, and reason about text? Whether used as a textbook or as a technical reference, it provides a structured path to mastering natural language processing in the deep learning era.

Zielgruppe

Graduate

Autoren/Hrsg.

Xiao, Tong

Zhu, Jingbo

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Natürliche Sprachen & Maschinelle Übersetzung
• Geisteswissenschaften Sprachwissenschaft Computerlinguistik, Korpuslinguistik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen

Inhaltsverzeichnis

.- Part I: Preliminaries(??).- Chapter 1: Foundations of Machine Learning.- Chapter 2: Foundations of Neural Networks.- Part II: Basic Models(????).- Chapter 3: Words and Word Vectors.- Chapter 4: Recurrent and Convolutional Sequence Models.- Chapter 5: Sequence-to-Sequence Models.- Part III: Transformers(Transformer ??).- Chapter 6: Attention and Transformers.- Chapter 7: Pre-training and Fine-tuning.- Part IV: Large Language Models(?????).- Chapter 8: Foundations of Large Language Models.- Chapter 9: Scaling, Training, and Inference.- Part V: Applications(??).- Chapter 10: Machine Translation.- Chapter 11: Dialogue Systems.- Chapter 12: Text Generation and Summarization.

Über Autor(innen)

Tong Xiao  is a Professor of Computer Science at Northeastern University (NEU), China, where he also received his Ph.D. He leads the university’s Natural Language Processing Lab and serves as Principal Investigator on multiple national research projects in machine translation and language modeling. As co-founder of the NiuTrans project, he has played a central role in developing scalable machine translation systems and large language models widely adopted in academia and industry. Dr. Xiao has authored over 100 peer-reviewed papers in top-tier conferences and journals, and his work has received several best paper awards.

Jingbo Zhu is a Professor of Computer Science at Northeastern University and the founder of the NiuTrans project. A pioneer in machine translation and natural language processing in China, he has led the development of one of the country’s most widely used translation systems, supporting over 450 languages. His research spans neural NLP, large-scale language modeling, and cross-lingual systems, with over 100 publications in leading venues.