Tracking Information Flow between Primary and Secondary News Sources

Will Radford, Ben Hachey, James R. Curran, Maria Milosavljevic

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

Abstract

Tracking information flow (IFLOW) is crucial to understanding the evolution of news stories. We present analysis and experiments for IFLOW between company announcements and newswire. Error analysis shows that many FPs are annotation errors and many FNs are due to coarse-grained document-level modelling. Experiments show that document meta-data features (e.g., category, length, timing) improve f-scores relative to upper bound by 23%.
Original languageEnglish
Title of host publicationProceedings of the Main Conference on Human Language Technologies
Subtitle of host publicationThe 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL HLT 2010)
Place of PublicationStroudsburg, PA
PublisherAssociation for Computational Linguistics
Pages29-30
Number of pages2
Publication statusPublished - 2010
EventNorth American Association for Computational Linguistics - Los Angeles, CA, United States
Duration: 2 Jun 20106 Jun 2010

Conference

ConferenceNorth American Association for Computational Linguistics
CountryUnited States
CityLos Angeles, CA
Period2/06/106/06/10

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  • Cite this

    Radford, W., Hachey, B., Curran, J. R., & Milosavljevic, M. (2010). Tracking Information Flow between Primary and Secondary News Sources. In Proceedings of the Main Conference on Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL HLT 2010) (pp. 29-30). Stroudsburg, PA: Association for Computational Linguistics.