A Commercial Perspective on Reference

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

I briefly describe some of the commercial work
which Arria NLG is doing in referring expression
algorithms, and highlight differences between
what is commercially important (at least
to Arria) and the NLG research literature. Arria’s
focus is on high-quality algorithms for
types of reference which are important in its
systems. These algorithms need to be parametrisable
for different genres and domains,
usable in hybrid systems which include some
canned text, and support variation.
Original languageEnglish
Title of host publicationProceedings of The 10th International Natural Language Generation conference
PublisherACL
Pages134-138
DOIs
Publication statusPublished - 2017
Event10th International Natural Language Generation conference - , Spain
Duration: 4 Sep 20177 Sep 2017

Conference

Conference10th International Natural Language Generation conference
CountrySpain
Period4/09/177/09/17

Cite this

Reiter, E. B. (2017). A Commercial Perspective on Reference. In Proceedings of The 10th International Natural Language Generation conference (pp. 134-138). ACL. https://doi.org/10.18653/v1/W17-3519

A Commercial Perspective on Reference. / Reiter, Ehud Baruch.

Proceedings of The 10th International Natural Language Generation conference. ACL, 2017. p. 134-138.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Reiter, EB 2017, A Commercial Perspective on Reference. in Proceedings of The 10th International Natural Language Generation conference. ACL, pp. 134-138, 10th International Natural Language Generation conference, Spain, 4/09/17. https://doi.org/10.18653/v1/W17-3519
Reiter EB. A Commercial Perspective on Reference. In Proceedings of The 10th International Natural Language Generation conference. ACL. 2017. p. 134-138 https://doi.org/10.18653/v1/W17-3519
Reiter, Ehud Baruch. / A Commercial Perspective on Reference. Proceedings of The 10th International Natural Language Generation conference. ACL, 2017. pp. 134-138
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note = "Many thanks to the many people on the Arria team (too many to list here) who have worked on developing, testing, and documenting the above-mentioned algorithms. My thanks also to Kees van Deemter and other members of the Aberdeen University CLAN research group for their very valuable advice and suggestions; and to the anonymous reviewers for their comments and suggestions.",
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