APART: Automatic Political Actor Recommendation in Real-time

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 algorit...

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Bibliographic Details
Published inSocial, Cultural, and Behavioral Modeling pp. 342 - 348
Main Authors Solaimani, Mohiuddin, Salam, Sayeed, Khan, Latifur, Brandt, Patrick T., D’Orazio, Vito
Format Book Chapter
LanguageEnglish
Published Cham Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
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Name Entity Recognition
Parse Tree
Recommended Actor
True Role
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Summary: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.
ISBN:331960239X
9783319602394
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-319-60240-0_42