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Finding Relevant Relations in Relevant Documents

  • Michael Schuhmacher
  • Benjamin Roth
  • Simone Paolo Ponzetto
  • Laura Dietz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9626)

Abstract

This work studies the combination of a document retrieval and a relation extraction system for the purpose of identifying query-relevant relational facts. On the TREC Web collection, we assess extracted facts separately for correctness and relevance. Despite some TREC topics not being covered by the relation schema, we find that this approach reveals relevant facts, and in particular those not yet known in the knowledge base DBpedia. The study confirms that mention frequency, document relevance, and entity relevance are useful indicators for fact relevance. Still, the task remains an open research problem.

Keywords

Query Expansion Test Collection Relation Extraction Document Retrieval Entity Relevance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This work was in part funded by the Deutsche Forschungsgemeinschaft within the JOIN-T project (research grant PO 1900/1-1), in part by DARPA under agreement number FA8750-13-2-0020, through the Elitepostdoc program of the BW-Stiftung, an Amazon AWS grant in education, and by the Center for Intelligent Information Retrieval. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect those of the sponsor. We are also thankful for the support of Amina Kadry and the helpful comments of the anonymous reviewers.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Michael Schuhmacher
    • 1
  • Benjamin Roth
    • 2
  • Simone Paolo Ponzetto
    • 1
  • Laura Dietz
    • 1
  1. 1.Data and Web Science GroupUniversity of MannheimMannheimGermany
  2. 2.College of Information and Computer ScienceUniversity of MassachusettsAmherstUSA

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