@article{lazic-etal-2015-plato,
title = "{P}lato: A Selective Context Model for Entity Resolution",
author = "Lazic, Nevena and
Subramanya, Amarnag and
Ringgaard, Michael and
Pereira, Fernando",
editor = "Collins, Michael and
Lee, Lillian",
journal = "Transactions of the Association for Computational Linguistics",
volume = "3",
year = "2015",
address = "Cambridge, MA",
publisher = "MIT Press",
url = "https://aclanthology.org/Q15-1036",
doi = "10.1162/tacl_a_00154",
pages = "503--515",
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 107 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.",
}
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<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 107 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.</abstract>
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%0 Journal Article
%T Plato: A Selective Context Model for Entity Resolution
%A Lazic, Nevena
%A Subramanya, Amarnag
%A Ringgaard, Michael
%A Pereira, Fernando
%J Transactions of the Association for Computational Linguistics
%D 2015
%V 3
%I MIT Press
%C Cambridge, MA
%F lazic-etal-2015-plato
%X 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 107 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.
%R 10.1162/tacl_a_00154
%U https://aclanthology.org/Q15-1036
%U https://doi.org/10.1162/tacl_a_00154
%P 503-515
Markdown (Informal)
[Plato: A Selective Context Model for Entity Resolution](https://aclanthology.org/Q15-1036) (Lazic et al., TACL 2015)
ACL