Natural Language Generation
May 1, 2024
Updated June 27, 2025
15 minute read
A Comprehensive Guide to Natural Language Generation (NLG)
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Find a path to becoming a Natural Language Generation. Learn more at:
OpenCourser.com/topic/l9ws65/natural
Reading list
We've selected 21 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Natural Language Generation.
This foundational text in the field of Natural Language Processing, with significant portions dedicated to language modeling and generation. It provides a comprehensive overview of the fundamental algorithms and techniques. While not solely focused on NLG, it offers essential background knowledge for anyone serious about the topic and is widely used as a textbook in academic settings.
Focuses on transformers, the architecture behind many state-of-the-art language models used in modern NLG. It provides practical guidance on using the Hugging Face library for various NLP tasks, including text generation. It's highly relevant for understanding and implementing contemporary NLG techniques.
Delves into transformer architectures and their application in NLP, including text generation. It covers various models like BERT and RoBERTa and provides practical guidance using popular deep learning frameworks. It's a valuable resource for understanding and implementing modern generative models.
Specifically addresses deep learning approaches for text production, a core aspect of NLG. It provides an overview of neural models for generating text from various inputs and explores task-specific challenges. It's a specialized resource for those interested in the deep learning techniques used in NLG.
This practical guide focuses on building NLP systems using Python libraries. It includes coverage of neural networks and generative techniques, making it relevant to modern NLG. It's a good resource for those who want to apply NLP concepts and build real-world applications, including text generation.
Serves as an excellent introduction to NLP with a strong practical focus using the NLTK library in Python. It covers fundamental concepts and provides hands-on examples for tasks related to text processing, which are foundational for NLG. It's particularly well-suited for those with some programming background looking to get started in the field.
This concise book provides a quick and accessible overview of language models, which are central to modern NLG. It's a good resource for quickly grasping the core concepts without getting bogged down in excessive detail. It's suitable for those who need a high-level understanding or a quick review.
Offers a practical guide to building NLP systems with a focus on real-world applications. It covers the entire NLP pipeline and discusses various techniques, including those relevant to generation. It's a good resource for understanding how NLP concepts are applied in practice.
This handbook offers a broad and comprehensive overview of NLP, covering both classical and statistical approaches. It includes sections relevant to language generation and provides a good reference for various techniques and applications. While it may not focus exclusively on the latest deep learning methods, it provides valuable context and a wide range of topics.
Covers practical text analysis techniques using Python, which are valuable for both understanding and generating text. While not solely focused on generation, it provides essential skills in working with text data and applying machine learning models to language, relevant for building NLG systems.
A classic in the field, this book provides a strong theoretical foundation in statistical methods for NLP. While published in 1999, the statistical concepts and linguistic background covered are still highly relevant for understanding the principles behind many modern NLG techniques. It's more theoretical and best suited for those with a solid mathematical background.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/l9ws65/natural
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