Empirical Methods in Natural Language Generation Data-oriented Methods and Empirical Evaluation /

• Corporate Author: SpringerLink (Online service)
• Other Authors: Krahmer, Emiel. (Editor), Theune, Marit͡. (Editor)
• Format: Electronic
• Language: English
• Published: Berlin, Heidelberg : Springer Berlin Heidelberg, 2010.
• Series: Lecture Notes in Computer Science, 5790
• Subjects: Computer science.; Database management.; Data mining.; Information storage and retrieval systems.; Information systems.; Multimedia systems.; Artificial intelligence.; Computer Science.; Artificial Intelligence (incl. Robotics).; Information Storage and Retrieval.; Information Systems Applications (incl.Internet).; Database Management.; Data Mining and Knowledge Discovery.; Multimedia Information Systems.
• Online Access: https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/978-3-642-15573-4

 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.