Corpus-based Referring Expressions Generation

Hilder Pereira, Eder Novais, André Mariotti, Ivandré Paraboni

Abstract
In Natural Language Generation, the task of attribute selection (AS) consists of determining the appropriate attribute-value pairs (or semantic properties) that represent the contents of a referring expression. Existing work on AS includes a wide range of algorithmic solutions to the problem, but the recent availability of corpora annotated with referring expressions data suggests that corpus-based AS strategies become possible as well. In this work we tentatively discuss a number of AS strategies using both semantic and surface information obtained from a corpus of this kind. Relying on semantic information, we attempt to learn both global and individual AS strategies that could be applied to a standard AS algorithm in order to generate descriptions found in the corpus. As an alternative, and perhaps less traditional approach, we also use surface information to build statistical language models of the referring expressions that are most likely to occur in the corpus, and let the model probabilities guide attribute selection.
Anthology ID:
L12-1025
Volume:
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)
Month:
May
Year:
2012
Address:
Istanbul, Turkey
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
4004–4009
Language:
URL:
http://www.lrec-conf.org/proceedings/lrec2012/pdf/152_Paper.pdf
DOI:
Bibkey:
Cite (ACL):
Hilder Pereira, Eder Novais, André Mariotti, and Ivandré Paraboni. 2012. Corpus-based Referring Expressions Generation. In Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12), pages 4004–4009, Istanbul, Turkey. European Language Resources Association (ELRA).
Cite (Informal):
Corpus-based Referring Expressions Generation (Pereira et al., LREC 2012)
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PDF:
http://www.lrec-conf.org/proceedings/lrec2012/pdf/152_Paper.pdf
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