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2014 | OriginalPaper | Buchkapitel

Syntax and Data-to-Text Generation

verfasst von : Claire Gardent

Erschienen in: Statistical Language and Speech Processing

Verlag: Springer International Publishing

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Abstract

With the development of the web of data, recent statistical, data-to-text generation approaches have focused on mapping data (e.g., database records or knowledge-base (KB) triples) to natural language. In contrast to previous grammar-based approaches, this more recent work systematically eschews syntax and learns a direct mapping between meaning representations and natural language. By contrast, I argue that an explicit model of syntax can help support NLG in several ways. Based on case studies drawn from KB-to-text generation, I show that syntax can be used to support supervised training with little training data; to ensure domain portability; and to improve statistical hypertagging.

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Nächstes Kapitel Spoken Language Processing: Time to Look Outside?
Fußnoten
2
This is modulo aggregation of relations. Thus two subject sharing relations may be realised in the same clause.
 
3
Fluency was rated on a scale from 0 to 5.
 
4
For all results discussed, we assume a hypertagging module returning up to 20 best solutions.
 
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Metadaten
Titel
Syntax and Data-to-Text Generation
verfasst von
Claire Gardent
Copyright-Jahr
2014
Buch
Statistical Language and Speech Processing

Print ISBN: 978-3-319-11396-8

Electronic ISBN: 978-3-319-11397-5

Copyright-Jahr: 2014

DOI
https://doi.org/10.1007/978-3-319-11397-5_1