@conference {12407,
	title = {Modeling perspective using adaptor grammars},
	booktitle = {Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing},
	series = {EMNLP {\textquoteright}10},
	year = {2010},
	month = {2010///},
	pages = {284 - 292},
	publisher = {Association for Computational Linguistics},
	organization = {Association for Computational Linguistics},
	address = {Stroudsburg, PA, USA},
	abstract = {Strong indications of perspective can often come from collocations of arbitrary length; for example, someone writing get the government out of my X is typically expressing a conservative rather than progressive viewpoint. However, going beyond unigram or bigram features in perspective classification gives rise to problems of data sparsity. We address this problem using nonparametric Bayesian modeling, specifically adaptor grammars (Johnson et al., 2006). We demonstrate that an adaptive na{\"\i}ve Bayes model captures multiword lexical usages associated with perspective, and establishes a new state-of-the-art for perspective classification results using the Bitter Lemons corpus, a collection of essays about mid-east issues from Israeli and Palestinian points of view.},
	url = {http://dl.acm.org/citation.cfm?id=1870658.1870686},
	author = {Hardisty,Eric A. and Jordan Boyd-Graber and Resnik, Philip}
}
