Detecting interesting event sequences for sports reporting

François Lareau*, Mark Dras, Robert Dale

*Corresponding author for this work

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

7 Citations (Scopus)
24 Downloads (Pure)

Abstract

Hand-crafted approaches to content determination are expensive to port to new domains. Machine-learned approaches, on the other hand, tend to be limited to relatively simple selection of items from data sets. We observe that in time series domains, textual descriptions often aggregate a series of events into a compact description. We present a simple technique for automatically determining sequences of events that are worth reporting, and evaluate its effectiveness.

Original languageEnglish
Title of host publicationENLG 2011 - 13th European Workshop on Natural Language Generation, Proceedings
Place of PublicationStroudsburg, PA
PublisherAssociation for Computational Linguistics (ACL)
Pages200-205
Number of pages6
Publication statusPublished - 2011
Event13th European Workshop on Natural Language Generation, ENLG 2011 - Nancy, France
Duration: 28 Sep 201130 Sep 2011

Other

Other13th European Workshop on Natural Language Generation, ENLG 2011
CountryFrance
CityNancy
Period28/09/1130/09/11

Bibliographical note

Copyright the Publisher 2011. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

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  • Cite this

    Lareau, F., Dras, M., & Dale, R. (2011). Detecting interesting event sequences for sports reporting. In ENLG 2011 - 13th European Workshop on Natural Language Generation, Proceedings (pp. 200-205). Stroudsburg, PA: Association for Computational Linguistics (ACL).