@inproceedings{L16-1088,
 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.
},
 address = {Portorož, Slovenia},
 author = {Maria Pershina and Yifan He and Ralph Grishman},
 booktitle = {Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016)},
 month = {May},
 pages = {556--560},
 publisher = {European Language Resources Association (ELRA)},
 title = {Entity Linking with a Paraphrase Flavor},
 url = {https://www.aclweb.org/anthology/L16-1088},
 year = {2016}
}