Plato: A Selective Context Model for Entity Resolution
Nevena Lazic, Amarnag Subramanya, Michael Ringgaard, Fernando Pereira
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.- Anthology ID:
- Q15-1036
- Volume:
- Transactions of the Association for Computational Linguistics, Volume 3
- Month:
- Year:
- 2015
- Address:
- Cambridge, MA
- Editors:
- Michael Collins, Lillian Lee
- Venue:
- TACL
- SIG:
- Publisher:
- MIT Press
- Note:
- Pages:
- 503–515
- Language:
- URL:
- https://aclanthology.org/Q15-1036
- DOI:
- 10.1162/tacl_a_00154
- Cite (ACL):
- Nevena Lazic, Amarnag Subramanya, Michael Ringgaard, and Fernando Pereira. 2015. Plato: A Selective Context Model for Entity Resolution. Transactions of the Association for Computational Linguistics, 3:503–515.
- Cite (Informal):
- Plato: A Selective Context Model for Entity Resolution (Lazic et al., TACL 2015)
- PDF:
- https://preview.aclanthology.org/corrections-2024-05/Q15-1036.pdf