@article{TACL637,
	author = {Nevena Lazic and Amarnag Subramanya and Michael Ringgaard and Fernando Pereira},
	title = {Plato: A Selective Context Model for Entity Resolution},
	journal = {Transactions of the Association for Computational Linguistics},
	volume = {3},
	year = {2015},
	keywords = {},
	abstract = {We present Plato, a probabilistic model for entity resolution that includes a novel approach for handling noisy or uninformative features, and supplements labeled training data derived from Wikipedia with a very large unlabeled text corpus. Training and inference in the proposed model can easily be distributed across many servers, allowing it to scale to over 10^7 entities. We evaluate Plato on three standard datasets for entity resolution. Our approach achieves the best results to-date on TAC KBP 2011 and is highly competitive on both the CoNLL 2003 and TAC KBP 2012 datasets.},
	issn = {2307-387X},
	url = {https://tacl2013.cs.columbia.edu/ojs/index.php/tacl/article/view/637},
	pages = {503--515}
}