Learning to Extract International Relations from Political Context Brendan O’Connor Brandon M. Stewart Noah A. Smith School of Computer Science Department of Government School of Computer Science Carnegie Mellon University Harvard University Carnegie Mellon University Pittsburgh, PA 15213, USA Cambridge, MA 02139, USA Pittsburgh, PA 15213, USA
[email protected] [email protected] [email protected] Abstract model of the relationship between a pair of polit- ical actors imposes a prior distribution over types We describe a new probabilistic model of linguistic events. Our probabilistic model in- for extracting events between major polit- fers latent frames, each a distribution over textual ical actors from news corpora. Our un- expressions of a kind of event, as well as a repre- supervised model brings together famil- sentation of the relationship between each political iar components in natural language pro- actor pair at each point in time. We use syntactic cessing (like parsers and topic models) preprocessing and a logistic normal topic model, with contextual political information— including latent temporal smoothing on the politi- temporal and dyad dependence—to in- cal context prior. fer latent event classes. We quantita- tively evaluate the model’s performance We apply the model in a series of compar- on political science benchmarks: recover- isons to benchmark datasets in political science. ing expert-assigned event class valences, First, we compare the automatically learned verb and detecting real-world conflict. We also classes to a pre-existing ontology and hand-crafted 1 conduct a small case study based on our verb patterns from TABARI, an open-source and model’s inferences.