Handling uncertainty in relation extraction: a case study on tennis tournament results extraction from tweets

Jochem Verburg, Mena Badieh Habib, Maurice van Keulen

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Abstract

Relation extraction involves different types of uncertainty due to the imperfection of the extraction tools and the inherent ambiguity of unstructured text. In this paper, we discuss several ways of handling uncertainties in relation extraction from social media. Our study case is to extract tennis games’ results for two Grand Slam tennis tournaments from tweets. Analysis has been done to find to what extent it is useful to use semantic web, domain knowledge, facts repetition, and authors’ trustworthiness to improve the certainty of the extracted relations.
Original languageUndefined
Title of host publicationProceedings of the 8th International Conference on Knowledge Capture (K-CAP 2015)
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
PagesArticle No. 26
Number of pages4
ISBN (Print)978-1-4503-3849-3
DOIs
Publication statusPublished - Oct 2015

Publication series

Name
PublisherACM

Keywords

  • EWI-26210
  • Tennis tournament
  • Social Media
  • Named Entity Linking
  • Named Entity Recognition
  • METIS-312694
  • Twitter
  • Uncertainty Analysis
  • Relation Extraction
  • IR-96989
  • Semantic Web

Cite this

Verburg, J., Habib, M. B., & van Keulen, M. (2015). Handling uncertainty in relation extraction: a case study on tennis tournament results extraction from tweets. In Proceedings of the 8th International Conference on Knowledge Capture (K-CAP 2015) (pp. Article No. 26). New York: Association for Computing Machinery (ACM). https://doi.org/10.1145/2815833.2816960