Empirical Methods in Natural Language Generation

Emiel Krahmer (Editor), Mariet Theune (Editor)

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    Abstract

    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.
    Original languageUndefined
    Place of PublicationBerlin
    PublisherSpringer
    Number of pages353
    ISBN (Print)978-3-642-15572-7
    DOIs
    Publication statusPublished - Sep 2010

    Publication series

    NameLecture Notes in Computer Science
    PublisherSpringer Verlag
    Volume5790

    Keywords

    • IR-73566
    • EWI-18528
    • METIS-271048

    Cite this

    Krahmer, E., & Theune, M. (Eds.) (2010). Empirical Methods in Natural Language Generation. (Lecture Notes in Computer Science; Vol. 5790). Berlin: Springer. https://doi.org/10.1007/978-3-642-15573-4