Entity Linking with a Paraphrase Flavor

Maria Pershina, Yifan He, Ralph Grishman


Abstract
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.
Anthology ID:
L16-1088
Volume:
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)
Month:
May
Year:
2016
Address:
Portorož, Slovenia
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
556–560
Language:
URL:
https://aclanthology.org/L16-1088
DOI:
Bibkey:
Cite (ACL):
Maria Pershina, Yifan He, and Ralph Grishman. 2016. Entity Linking with a Paraphrase Flavor. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 556–560, Portorož, Slovenia. European Language Resources Association (ELRA).
Cite (Informal):
Entity Linking with a Paraphrase Flavor (Pershina et al., LREC 2016)
Copy Citation:
PDF:
https://aclanthology.org/L16-1088.pdf
Code
 masha-p/paraphrase_flavor
Data
PIT