Empirical Methods in Natural Language Generation Data-oriented M
Natural language generation (NLG) is a subfield of natural language processing (NLP) that is often characterized as the study of automatically converting non-linguistic representations (e.g., from databases or other knowledge sources) into coherent natura
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Subseries of Lecture Notes in Computer Science
5790
Emiel Krahmer Mariët Theune (Eds.)
Empirical Methods in Natural Language Generation Data-Oriented Methods and Empirical Evaluation
13
Series Editors Randy Goebel, University of Alberta, Edmonton, Canada Jörg Siekmann, University of Saarland, Saarbrücken, Germany Wolfgang Wahlster, DFKI and University of Saarland, Saarbrücken, Germany Volume Editors Emiel Krahmer Tilburg University, Tilburg Center for Cognition and Communication (TiCC) Faculty of Humanities Department of Communication and Information Sciences (DCI) P.O.Box 90153, 5000 LE Tilburg, The Netherlands E-mail: [email protected] Mariët Theune University of Twente, Human Media Interaction (HMI) Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) P.O. Box 217, 7500 AE Enschede, The Netherlands E-mail: [email protected]
Library of Congress Control Number: 2010933310 CR Subject Classification (1998): I.2, H.3, H.4, H.2, H.5, J.1 LNCS Sublibrary: SL 7 – Artificial Intelligence ISSN ISBN-10 ISBN-13
0302-9743 3-642-15572-3 Springer Berlin Heidelberg New York 978-3-642-15572-7 Springer Berlin Heidelberg New York
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Preface
Natural language generation (NLG) is a subfield of natural language processing (NLP) that is often characterized as the study of automatically converting non-linguistic representations (e.g., from databases or other knowledge sources) into coherent natural language text. NLG is useful for many practical applications, ranging from automatically generated weather forecasts to summarizing medical information in a patient-friendly way, but is also interesting from a theoretical perspective, as it offers new, computational insights into the process of human language production in general. Sometimes, NLG is framed as the mirror image of natural language understanding (NLU), but in fact the respective problems and solutions are rather dissimilar: while NLU is basically a disambiguation problem, where ambiguous natural language inputs are mapped onto unambiguous representations, NLG is more like a choice problem, where it has to be decided which words and sentences best express certain specific concepts. Arguably the most comprehensive currently available text book on NLG is Reiter a
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