Read e-book online Building Natural Language Generation Systems PDF

By Ehud Reiter

ISBN-10: 0511519850

ISBN-13: 9780511519857

ISBN-10: 0521620368

ISBN-13: 9780521620369

This e-book explains the way to construct traditional Language iteration (NLG) systems--computer software program structures that instantly generate comprehensible texts in English or different human languages. NLG platforms use wisdom approximately language and the appliance area to immediately produce files, studies, factors, aid messages, and other forms of texts. The e-book covers the algorithms and representations had to practice the center initiatives of rfile making plans, microplanning, and floor recognition, utilizing a case learn to teach how those elements healthy jointly. it truly is crucial studying for researchers attracted to NLP, AI, and HCI; and for builders drawn to complex document-creation expertise.

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1994). Although Kay, Gawron, and Norvig (1994) describe one particular machine translation exercise, their book contains a wealth of relevant background information. Pointers to further reading on each of the specific aspects of NLG covered in this book are provided at the end of each chapter. Natural Language Generation in Practice Before commencing our exploration of the technical content of work in natural language generation, in this chapter we take a step back and consider questions that arise when we look at putting NLG technology to work.

Natural Language Generation in Practice Before commencing our exploration of the technical content of work in natural language generation, in this chapter we take a step back and consider questions that arise when we look at putting NLG technology to work. We consider alternatives to NLG, and we discuss the circumstances under which it is appropriate to use NLG technology. We also look at requirements analysis and the evaluation and fielding of NLG systems. These topics are rarely discussed in the research literature but are of major significance when we are concerned with the construction of an operational natural language generation system.

The results showed that while the human-authored letters were perceived to be the best, the NLG system produced significantly better letters than the mail-merge system. The mail-merge system sometimes produced letters that were incomprehensible or grammatically incorrect, which the NLG system never did; and even when the mail-merge system did produce comprehensible and correct letters, the judges evaluated the NLG letters as being much better in terms of flow, personalisation, precise use of terminology, and other measures of quality.

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Building Natural Language Generation Systems by Ehud Reiter


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