This book explains how to build Natural Language Generation (NLG) systems--computer software systems that automatically generate understandable texts in English or other human languages. NLG systems use knowledge about language and the application domain to automatically produce documents, reports, explanations, help messages, and other kinds of texts. The book covers the algorithms and representations needed to perform the core tasks of document planning, microplanning, and surface realization, using a case study to show how these components fit together. It is essential reading for researchers interested in NLP, AI, and HCI; and for developers interested in advanced document-creation technology.
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Editorial Reviews
Review
"Consistent and well-developed...a valuable guide..." General Linguistics
"[A]n indispensable guide...extremely well-organized and well-written." User Modeling and User-Adapted Interaction
"Each topic is clearly presented...the information is very well organized...It is detailed, without being too dense or overloaded with technical jargon...This book should be read by anyone interested in natural language processing who wants to get a better idea of the specifics of NLG." Computing Reviews
"[A]n indispensable guide...extremely well-organized and well-written." User Modeling and User-Adapted Interaction
"Each topic is clearly presented...the information is very well organized...It is detailed, without being too dense or overloaded with technical jargon...This book should be read by anyone interested in natural language processing who wants to get a better idea of the specifics of NLG." Computing Reviews
Book Description
This book explains how to build Natural Language Generation (NLG) systems--computer software systems which automatically generate understandable texts in English or other human languages. NLG systems use knowledge about language and the application domain to automatically produce documents, reports, explanations, help messages, and other kinds of texts. The book covers the algorithms and representations needed to perform the core tasks of document planning, microplanning, and surface realization, using a case study to show how these components fit together.It is essential reading for researchers interested in NLP, AI, and HCI; and for developers interested in advanced document-creation technology.
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Summary: Reiter and Dale provide a useful introduction to NLG.
Review: This book is probably the first of its kind. Although there has been considerable work in natural language summarization, research in producing natural language is in its infancy. The authors draw upon actual NLG systems to illustrate the techinical issues involved. They are careful to point out that NLG may not be the best solution for the reader's practical problems. For instance, human writers might produce better text, or mail-merge programs might do the job (albeit with lower quality) at a significantly lower price. A shortcoming -- perhaps a necessary one -- is that the authors hesitate to describe research and development trends for the past decade. The reason does not appear to be a shortage of the quality and quantity of NLG R&D, because the authors do analyze trends for the 1970's and the 1980's. Are the authors hesitant to criticize their contemporary colleagues? A blunt trends analysis would have been helpful, particularly for readers new to the field.
Inside This Book
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First Sentence:
NATURAL LANGUAGE GENERATION (NLG) is the subfield of artificial intelligence and computational linguistics that focuses on computer systems that can produce understandable texts in English or other human languages. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
document presentation system, content determination process, target text corpus, referring expression generation, content determination rules, document planner, surface realiser, front gear mechanism, bidirectional grammars, realisation statements, phrase specifications, realisation component, multimodal documents, linguistic realisation, lexicalisation process, textual constituents, surface realisation, natural language generation system, abstract syntactic structures, distinguishing description, generating referring expressions, canned text, document structuring, structure realisation, daily weather records Key Phrases - Capitalized Phrases (CAPs): (learn more)
Upper Model, Systemic Functional Grammar, World Wide Web, Microsoft Word, African Elephant, Indian Elephant, The Elephant, Joe Blackford, Mars Creek, Mike Williams, Netscape Navigator, Richard Adams, Simplified English, Tom Jones, Courtesy of Elizabeth, Courtesy of Maria Milosavljevic, Sequence Rule, Welcome Describe Compare User Profile New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
NATURAL LANGUAGE GENERATION (NLG) is the subfield of artificial intelligence and computational linguistics that focuses on computer systems that can produce understandable texts in English or other human languages. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
document presentation system, content determination process, target text corpus, referring expression generation, content determination rules, document planner, surface realiser, front gear mechanism, bidirectional grammars, realisation statements, phrase specifications, realisation component, multimodal documents, linguistic realisation, lexicalisation process, textual constituents, surface realisation, natural language generation system, abstract syntactic structures, distinguishing description, generating referring expressions, canned text, document structuring, structure realisation, daily weather records Key Phrases - Capitalized Phrases (CAPs): (learn more)
Upper Model, Systemic Functional Grammar, World Wide Web, Microsoft Word, African Elephant, Indian Elephant, The Elephant, Joe Blackford, Mars Creek, Mike Williams, Netscape Navigator, Richard Adams, Simplified English, Tom Jones, Courtesy of Elizabeth, Courtesy of Maria Milosavljevic, Sequence Rule, Welcome Describe Compare User Profile New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 59
books:
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24
books
cite this book:
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- Computational Linguistics: An Introduction (Studies in Natural Language Processing) by Ralph Grishman on 5 pages
- Intelligent Multimedia Interfaces by Mark T. Maybury in Back Matter (1), Back Matter (2), and Back Matter (3)
- Text Generation (Studies in Natural Language Processing) by Kathleen McKeown in Front Matter, Back Matter (1), and Back Matter (2)
- Artificial Intelligence (3rd Edition) by Winston in Back Matter (1), and Back Matter (2)
- Theoretical Issues in Natural Language Processing by Yorick Wilks in Back Matter (1), and Back Matter (2)
See all 59 books this book cites
- Multimodal Intelligent Information Presentation (Text, Speech and Language Technology) by Oliviero Stock on page 175, and page 201
- Natural Language Generation: Third International Conference, INLG 2004, Brockenhurst, UK, July 14-16, 2004, Proceedings (Lecture Notes in Computer Science) by Anja Belz on page 89, and page 170
- Applications and Innovations in Intelligent Systems IX: Proceedings of ES2001, the Twenty-first SGES International Conference on Knowledge Based ... Intelligence, Cambridge, December 2001 by Ann Macintosh on page 196
- Logics of Conversation (Studies in Natural Language Processing) by Nicholas Asher in Front Matter
- A Computational Theory of Writing Systems (Studies in Natural Language Processing) by Richard Sproat in Front Matter
See all 24 books citing this book
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