Plato: A Selective Context Model for Entity Resolution
Venue
Transactions of the Association for Computational Linguistics, vol. 3 (2015), pp. 503-515
Publication Year
2015
Authors
Nevena Lazic, Amarnag Subramanya, Michael Ringgaard, Fernando Pereira
BibTeX
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.
