Empirical Methods in Natural Language Generation

Empirical Methods in Natural Language Generation

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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.The alignment of answers to question types as a semantic role labelling task can also employ the same method [28]. ... We treat the attachment of modifiers to heads as an assignment problem, using the Kuhn-Munkres assignment algorithm .


Title:Empirical Methods in Natural Language Generation
Author: Emiel Krahmer
Publisher:Springer Science & Business Media - 2010-09-09
ISBN-13:

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