Text Generation


Book Description

Kathleen McKeown explores natural language text and presents a formal analysis of problems in a computer program, TEXT.




User Modelling in Text Generation


Book Description

This book addresses the issue of how the user's level of domain knowledge affects interaction with a computer system. It demonstrates the feasibility of incorporating a model of user's domain knowledge into a natural language generation system.




Teaching Generation Text


Book Description

Mobilizing the power of cell phones to maximize students' learning power Teaching Generation Text shows how teachers can turn cell phones into an educational opportunity instead of an annoying distraction. With a host of innovative ideas, activities, lessons, and strategies, Nielsen and Webb offer a unique way to use students' preferred method of communication in the classroom. Cell phones can remind students to study, serve as a way to take notes, provide instant, on-demand answers and research, be a great vehicle for home-school connection, and record and capture oral reports or responses to polls and quizzes, all of which can be used to enhance lesson plans and increase motivation. Offers tactics for teachers to help their students integrate digital technology with their studies Filled with research-based ideas and strategies for using a cell phone to enhance learning Provides methods for incorporating cell phones into instruction with a unit planning guide and lesson plan ideas This innovative new book is filled with new ideas for engaging learners in fun, free, and easy ways using nothing more than a basic, text-enabled cell phone.




Lexical Semantics and Knowledge Representation in Multilingual Text Generation


Book Description

In knowledge-based natural language generation, issues of formal knowledge representation meet with the linguistic problems of choosing the most appropriate verbalization in a particular situation of utterance. Lexical Semantics and Knowledge Representation in Multilingual Text Generation presents a new approach to systematically linking the realms of lexical semantics and knowledge represented in a description logic. For language generation from such abstract representations, lexicalization is taken as the central step: when choosing words that cover the various parts of the content representation, the principal decisions on conveying the intended meaning are made. A preference mechanism is used to construct the utterance that is best tailored to parameters representing the context. Lexical Semantics and Knowledge Representation in Multilingual Text Generation develops the means for systematically deriving a set of paraphrases from the same underlying representation with the emphasis on events and verb meaning. Furthermore, the same mapping mechanism is used to achieve multilingual generation: English and German output are produced in parallel, on the basis of an adequate division between language-neutral and language-specific (lexical and grammatical) knowledge. Lexical Semantics and Knowledge Representation in Multilingual Text Generation provides detailed insights into designing the representations and organizing the generation process. Readers with a background in artificial intelligence, cognitive science, knowledge representation, linguistics, or natural language processing will find a model of language production that can be adapted to a variety of purposes.




LLMs


Book Description

"LLMs: From Origin to Present and Future Applications" by Ronald Legarski is an authoritative exploration of Large Language Models (LLMs) and their profound impact on artificial intelligence, machine learning, and various industries. This comprehensive guide traces the evolution of LLMs from their early beginnings to their current applications, and looks ahead to their future potential across diverse fields. Drawing on extensive research and industry expertise, Ronald Legarski provides readers with a detailed understanding of how LLMs have developed, the technologies that power them, and the transformative possibilities they offer. This book is an invaluable resource for AI professionals, researchers, and enthusiasts who want to grasp the intricacies of LLMs and their applications in the modern world. Key topics include: The Origins of LLMs: A historical perspective on the development of natural language processing and the key milestones that led to the creation of LLMs. Technological Foundations: An in-depth look at the architecture, data processing, and training techniques that underpin LLMs, including transformer models, tokenization, and attention mechanisms. Current Applications: Exploration of how LLMs are being used today in industries such as healthcare, legal services, education, content creation, and more. Ethical Considerations: A discussion on the ethical challenges and societal impacts of deploying LLMs, including bias, fairness, and the need for responsible AI governance. Future Directions: Insights into the future of LLMs, including their role in emerging technologies, interdisciplinary research, and the potential for creating more advanced AI systems. With clear explanations, practical examples, and forward-thinking perspectives, "LLMs: From Origin to Present and Future Applications" equips readers with the knowledge to navigate the rapidly evolving field of AI. Whether you are a seasoned AI professional, a researcher in the field, or someone with an interest in the future of technology, this book offers a thorough exploration of LLMs and their significance in the digital age. Discover how LLMs are reshaping industries, driving innovation, and what the future holds for these powerful AI models.




Natural Language Generation


Book Description

Proceedings of the NATO Advanced Research Workshop, Nijmegen, The Netherlands, August 19-23, 1986




Advanced Applications of Generative AI and Natural Language Processing Models


Book Description

The rapid advancements in Artificial Intelligence (AI), specifically in Natural Language Processing (NLP) and Generative AI, pose a challenge for academic scholars. Staying current with the latest techniques and applications in these fields is difficult due to their dynamic nature, while the lack of comprehensive resources hinders scholars' ability to effectively utilize these technologies. Advanced Applications of Generative AI and Natural Language Processing Models offers an effective solution to address these challenges. This comprehensive book delves into cutting-edge developments in NLP and Generative AI. It provides insights into the functioning of these technologies, their benefits, and associated challenges. Targeting students, researchers, and professionals in AI, NLP, and computer science, this book serves as a vital reference for deepening knowledge of advanced NLP techniques and staying updated on the latest advancements in generative AI. By providing real-world examples and practical applications, scholars can apply their learnings to solve complex problems across various domains. Embracing Advanced Applications of Generative AI and Natural Language Processing Modelsequips academic scholars with the necessary knowledge and insights to explore innovative applications and unleash the full potential of generative AI and NLP models for effective problem-solving.




Past, Present, and Future Contributions of Cognitive Writing Research to Cognitive Psychology


Book Description

This volume tells the story of research on the cognitive processes of writing--from the perspectives of the early pioneers, the contemporary contributors, and visions of the future for the field. It includes the very latest in findings from neuroscience and experimental cognitive psychology, and provides the most comprehensive current overview on this topic.




Prompt Engineering for Generative AI


Book Description

Large language models (LLMs) and diffusion models such as ChatGPT and Stable Diffusion have unprecedented potential. Because they have been trained on all the public text and images on the internet, they can make useful contributions to a wide variety of tasks. And with the barrier to entry greatly reduced today, practically any developer can harness LLMs and diffusion models to tackle problems previously unsuitable for automation. With this book, you'll gain a solid foundation in generative AI, including how to apply these models in practice. When first integrating LLMs and diffusion models into their workflows, most developers struggle to coax reliable enough results from them to use in automated systems. Authors James Phoenix and Mike Taylor show you how a set of principles called prompt engineering can enable you to work effectively with AI. Learn how to empower AI to work for you. This book explains: The structure of the interaction chain of your program's AI model and the fine-grained steps in between How AI model requests arise from transforming the application problem into a document completion problem in the model training domain The influence of LLM and diffusion model architecture—and how to best interact with it How these principles apply in practice in the domains of natural language processing, text and image generation, and code




The Fluency Construct


Book Description

This book provides a comprehensive overview of fluency as a construct and its assessment in the context of curriculum-based measurement (CBM). Comparing perspectives from language acquisition, reading, and mathematics, the book parses the vagueness and complexities surrounding fluency concepts and their resulting impact on testing, intervention, and students' educational development. Applications of this knowledge in screening and testing, ideas for creating more targeted measures, and advanced methods for studying fluency data demonstrate the overall salience of fluency within CBM. Throughout, contributors argue for greater specificity and nuance in isolating skills to be measured and improved, and for terminology that reflects those educational benchmarks. Included in the coverage: Indicators of fluent writing in beginning writers. Fluency in language acquisition, reading, and mathematics. Foundations of fluency-based assessments in behavioral and psychometric paradigms. Using response time and accuracy data to inform the measurement of fluency. Using individual growth curves to model reading fluency. Latent class analysis for reading fluency research. The Fluency Construct: Curriculum-Based Measurement Concepts and Applications is an essential resource for researchers, graduate students, and professionals in clinical child and school psychology, language and literature, applied linguistics, special education, neuropsychology, and social work.