@inproceedings{c117366977e74a7da01fe96620bd7a8e,
title = "Learning to extract international relations from political context",
abstract = "We describe a new probabilistic model for extracting events between major political actors from news corpora. Our unsupervised model brings together familiar components in natural language processing (like parsers and topic models) with contextual political information-temporal and dyad dependence- to infer latent event classes. We quantitatively evaluate the model's performance on political science benchmarks: recovering expert-assigned event class valences, and detecting real-world conflict. We also conduct a small case study based on our model's inferences.",
author = "Brendan O'Connor and Stewart, {Brandon Michael} and Smith, {Noah A.}",
year = "2013",
language = "English (US)",
isbn = "9781937284503",
series = "ACL 2013 - 51st Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference",
publisher = "Association for Computational Linguistics (ACL)",
pages = "1094--1104",
booktitle = "Long Papers",
note = "51st Annual Meeting of the Association for Computational Linguistics, ACL 2013 ; Conference date: 04-08-2013 Through 09-08-2013",
}