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APART: Automatic Political Actor Recommendation in Real-time

  • Mohiuddin Solaimani
  • Sayeed Salam
  • Latifur Khan
  • Patrick T. Brandt
  • Vito D’Orazio
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10354)

Abstract

Extracting actor data from news reports is important when generating event data. Hand-coded dictionaries are used to code actors and actions. Manually updating dictionaries for new actors and roles is costly and there is no automated method. We propose a dynamic frequency-based actor ranking algorithm with partial string matching for new actor-role detection, based on similar actors in the CAMEO dictionary. This is compared to a graph-based weighted label propagation baseline method. Results show our method outperforms the alternatives.

Keywords

Edit Distance Parse Tree Recommended Actor Name Entity Recognition True Role 
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

Acknowledgments

Support from the National Science Foundation (NSF) SBE-SMA-1539302, CNS-1229652, and SBE-SES-1528624; and the Air Force Office of Scientific Research (AFOSR): FA-9550-12-1-0077. Any opinions, findings, and conclusions or recommendations expressed here are those of the authors and do not necessarily reflect the views of the NSF or the AFOSR.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of CSThe University of Texas at DallasRichardsonUSA
  2. 2.School of Economic, Political, and Policy SciencesThe University of Texas at DallasRichardsonUSA

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