Nevena Lazic


2016

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Collective Entity Resolution with Multi-Focal Attention
Amir Globerson | Nevena Lazic | Soumen Chakrabarti | Amarnag Subramanya | Michael Ringgaard | Fernando Pereira
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2015

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Embedding Methods for Fine Grained Entity Type Classification
Dani Yogatama | Daniel Gillick | Nevena Lazic
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

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Plato: A Selective Context Model for Entity Resolution
Nevena Lazic | Amarnag Subramanya | Michael Ringgaard | Fernando Pereira
Transactions of the Association for Computational Linguistics, Volume 3

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.