Natural Language Generation
May 1, 2024
Updated June 27, 2025
15 minute read
A Comprehensive Guide to Natural Language Generation (NLG)
Natural Language Generation, or NLG, is a fascinating subfield of artificial intelligence that focuses on a single, powerful goal: teaching computers to write. It is the process by which a machine takes structured data and transforms it into human-readable text. Whether it's turning raw weather data into a forecast you can understand, converting a spreadsheet of financial figures into a quarterly earnings report, or generating a personalized email, NLG is the technology working behind the scenes to create coherent, contextual, and often creative language.
The allure of working with NLG lies in its unique blend of creativity and technical rigor. Imagine building a system that can help a journalist draft an article by summarizing complex reports, or designing a chatbot that not only answers questions but does so with a distinct personality. It is a field that sits at the intersection of computer science, linguistics, and user experience, offering opportunities to solve practical business problems while pushing the boundaries of machine-human communication. For those with a passion for language and a knack for logical systems, a journey into Natural Language Generation can be an exceptionally rewarding path, full of challenges that are both intellectually stimulating and practically impactful.
Core Concepts and Terminology
To truly understand Natural Language Generation, it's helpful to become familiar with its foundational ideas and vocabulary. These concepts are the building blocks upon which all modern NLG systems are constructed. Grasping them will provide a solid base for exploring more advanced topics.
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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 recent book specifically on Natural Language Generation by a leading expert in the field. It provides a comprehensive overview of NLG, including modern approaches like language models, alongside traditional methods. It covers the full spectrum of NLG tasks, evaluation, and ethical considerations, making it highly relevant to contemporary NLG.
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.
Focuses on deep learning and neural network approaches to NLG and valuable resource for researchers and practitioners in the field.
Focuses specifically on the application of neural network models to NLP tasks. It covers essential concepts in deep learning as applied to language, which are crucial for understanding modern NLG techniques, especially those involving large language models. It's more focused than general NLP introductions and is valuable for those wanting to delve into the neural aspects.
Explores the application of deep learning techniques specifically within the context of NLP. It would cover various deep learning models and their use in tasks relevant to NLG, providing a more focused view than general deep learning texts. It's suitable for those with a background in NLP or machine learning looking to specialize in deep learning approaches.
Provides a practical introduction to deep learning using Python and the Keras library. It includes examples and explanations relevant to text processing and generative models, making it a valuable resource for implementing deep learning techniques for NLG. It's more accessible than the 'Deep Learning' book and is suitable for practitioners.
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.
While not exclusively about NLP, this seminal textbook on deep learning, the technology powering many recent advancements in NLG, particularly large language models. It provides a comprehensive theoretical foundation in deep learning architectures and training methods essential for understanding state-of-the-art generative models. It challenging but crucial read for those wanting to understand the underlying mechanisms.
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.
Provides a comprehensive overview of NLG in French and covers both theoretical and practical aspects of the field.
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.
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.
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.
Provides essential linguistic background for NLP, focusing on morphology and syntax. A solid understanding of linguistics is beneficial for building more sophisticated NLG systems that produce grammatically correct and natural-sounding text. It's valuable as supplementary reading to strengthen linguistic foundations.
While focused on text mining using R, this book covers fundamental text processing and analysis techniques that are relevant to understanding the input and output of NLG systems. It provides a different programming language perspective compared to Python-focused books and can be useful for those working in an R environment.
For more information about how these books relate to this course, visit:
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