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E-RESOURCE

Title Empirical methods in natural language generation [electronic resource] : data-oriented methods and empirical evaluation / Emiel Krahmer, Mariët Theune (eds.).

Published Berlin : Springer, 2010.

Copies

Location Call No. Status
 UniM INTERNET resource    AVAILABLE
Physical description 1 online resource (x, 352 p.) : ill.
Series Lecture notes in computer science, 0302-9743 ; 5790. Lecture notes in artificial intelligence
LNCS sublibrary. SL 7, Artificial intelligence
Lecture notes in computer science ; 5790. 0302-9743
Lecture notes in computer science. Lecture notes in artificial intelligence.
LNCS sublibrary. SL 7, Artificial intelligence.
Springer Lecture Notes in Computer Science
Bibliography Includes bibliographical references and index.
Summary 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. In recent years the field has evolved substantially. Perhaps the most important new development is the current emphasis on data-oriented methods and empirical evaluation. Progress in related areas such as machine translation, dialogue system design and automatic text summarization and the resulting awareness of the importance of language generation, the increasing availability of suitable corpora in recent years, and the organization of shared tasks for NLG, where different teams of researchers develop and evaluate their algorithms on a shared, held out data set have had a considerable impact on the field, and this book offers the first comprehensive overview of recent empirically oriented NLG research.
Other author Krahmer, Emiel, 1968-
Theune, Mariët, 1971-
SpringerLink issuing body.
Subject Natural language processing (Computer science) -- Congresses.
Computational linguistics -- Congresses.
Electronic books.
Conference papers and proceedings.
ISBN 9783642155734
3642155731
Standard Number 10.1007/978-3-642-15573-4