Maria Pershina


2016

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Entity Linking with a Paraphrase Flavor
Maria Pershina | Yifan He | Ralph Grishman
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

The task of Named Entity Linking is to link entity mentions in the document to their correct entries in a knowledge base and to cluster NIL mentions. Ambiguous, misspelled, and incomplete entity mention names are the main challenges in the linking process. We propose a novel approach that combines two state-of-the-art models ― for entity disambiguation and for paraphrase detection ― to overcome these challenges. We consider name variations as paraphrases of the same entity mention and adopt a paraphrase model for this task. Our approach utilizes a graph-based disambiguation model based on Personalized Page Rank, and then refines and clusters its output using the paraphrase similarity between entity mention strings. It achieves a competitive performance of 80.5% in B3+F clustering score on diagnostic TAC EDL 2014 data.

2015

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Personalized Page Rank for Named Entity Disambiguation
Maria Pershina | Yifan He | Ralph Grishman
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Idiom Paraphrases: Seventh Heaven vs Cloud Nine
Maria Pershina | Yifan He | Ralph Grishman
Proceedings of the First Workshop on Linking Computational Models of Lexical, Sentential and Discourse-level Semantics

2014

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Infusion of Labeled Data into Distant Supervision for Relation Extraction
Maria Pershina | Bonan Min | Wei Xu | Ralph Grishman
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)