Automatic generation of weather forecast texts using comprehensive probabilistic generation-space models

BELZ, ANJA (2008) Automatic generation of weather forecast texts using comprehensive probabilistic generation-space models Natural Language Engineering, 14 (4). pp. 431-455. ISSN 1351-3249

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Abstract

Two important recent trends in nlg are (i) probabilistic techniques and (ii) comprehensive approaches that move away from traditional strictly modular and sequential models. This paper reports experiments in which p cru — a generation framework that combines probabilistic generation methodology with a comprehensive model of the generation space — was used to semi-automatically create five different versions of a weather forecast generator. The generators were evaluated in terms of output quality, development time and computational efficiency against (i) human forecasters, (ii) a traditional handcrafted pipelined nlg system, and (iii) a halogen-style statistical generator. The most striking result is that despite acquiring all decision-making abilities automatically, the best p cru generators produce outputs of high enough quality to be scored more highly by human judges than forecasts written by experts.

Item Type: Journal article
Additional Information: © Cambridge University Press 2007
Uncontrolled Keywords: Natural language generation
Subjects: G000 Computing and Mathematical Sciences > G400 Computing
Q000 Languages and Literature - Linguistics and related subjects
DOI (a stable link to the resource): 10.1017/S1351324907004664
Faculties: Faculty of Science and Engineering > School of Computing, Engineering and Mathematics > Natural Language Technology
Depositing User: Converis
Date Deposited: 14 Nov 2007
Last Modified: 21 May 2014 11:01
URI: http://eprints.brighton.ac.uk/id/eprint/3092

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