Paper: Learning to Extract International Relations from Political Context

ACL ID P13-1108
Title Learning to Extract International Relations from Political Context
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2013
Authors

We describe a new probabilistic model for extracting events between major polit- ical actors from news corpora. Our un- supervised model brings together famil- iar components in natural language pro- cessing (like parsers and topic models) with contextual political information? temporal and dyad dependence?to in- fer latent event classes. We quantita- tively evaluate the model?s performance on political science benchmarks: recover- ing expert-assigned event class valences, and detecting real-world conflict. We also conduct a small case study based on our model?s inferences. A supplementary appendix, and replica- tion software/data are available online, at: http://brenocon.com/irevents