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.
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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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Solaimani, M., Salam, S., Khan, L., Brandt, P.T., D’Orazio, V. (2017). APART: Automatic Political Actor Recommendation in Real-time. In: Lee, D., Lin, YR., Osgood, N., Thomson, R. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2017. Lecture Notes in Computer Science(), vol 10354. Springer, Cham. https://doi.org/10.1007/978-3-319-60240-0_42
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DOI: https://doi.org/10.1007/978-3-319-60240-0_42
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