@inproceedings{charton-etal-2014-improving,
title = "Improving Entity Linking using Surface Form Refinement",
author = "Charton, Eric and
Meurs, Marie-Jean and
Jean-Louis, Ludovic and
Gagnon, Michel",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Declerck, Thierry and
Loftsson, Hrafn and
Maegaard, Bente and
Mariani, Joseph and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)",
month = may,
year = "2014",
address = "Reykjavik, Iceland",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2014/pdf/899_Paper.pdf",
pages = "4609--4615",
abstract = "In this paper, we present an algorithm for improving named entity resolution and entity linking by using surface form generation and rewriting. Surface forms consist of a word or a group of words that matches lexical units like Paris or New York City. Used as matching sequences to select candidate entries in a knowledge base, they contribute to the disambiguation of those candidates through similarity measures. In this context, misspelled textual sequences (entities) can be impossible to identify due to the lack of available matching surface forms. To address this problem, we propose an algorithm for surface form refinement based on Wikipedia resources. The approach extends the surface form coverage of our entity linking system, and rewrites or reformulates misspelled mentions (entities) prior to starting the annotation process. The algorithm is evaluated on the corpus associated with the monolingual English entity linking task of NIST KBP 2013. We show that the algorithm improves the entity linking system performance.",
}
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<abstract>In this paper, we present an algorithm for improving named entity resolution and entity linking by using surface form generation and rewriting. Surface forms consist of a word or a group of words that matches lexical units like Paris or New York City. Used as matching sequences to select candidate entries in a knowledge base, they contribute to the disambiguation of those candidates through similarity measures. In this context, misspelled textual sequences (entities) can be impossible to identify due to the lack of available matching surface forms. To address this problem, we propose an algorithm for surface form refinement based on Wikipedia resources. The approach extends the surface form coverage of our entity linking system, and rewrites or reformulates misspelled mentions (entities) prior to starting the annotation process. The algorithm is evaluated on the corpus associated with the monolingual English entity linking task of NIST KBP 2013. We show that the algorithm improves the entity linking system performance.</abstract>
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%0 Conference Proceedings
%T Improving Entity Linking using Surface Form Refinement
%A Charton, Eric
%A Meurs, Marie-Jean
%A Jean-Louis, Ludovic
%A Gagnon, Michel
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Declerck, Thierry
%Y Loftsson, Hrafn
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14)
%D 2014
%8 May
%I European Language Resources Association (ELRA)
%C Reykjavik, Iceland
%F charton-etal-2014-improving
%X In this paper, we present an algorithm for improving named entity resolution and entity linking by using surface form generation and rewriting. Surface forms consist of a word or a group of words that matches lexical units like Paris or New York City. Used as matching sequences to select candidate entries in a knowledge base, they contribute to the disambiguation of those candidates through similarity measures. In this context, misspelled textual sequences (entities) can be impossible to identify due to the lack of available matching surface forms. To address this problem, we propose an algorithm for surface form refinement based on Wikipedia resources. The approach extends the surface form coverage of our entity linking system, and rewrites or reformulates misspelled mentions (entities) prior to starting the annotation process. The algorithm is evaluated on the corpus associated with the monolingual English entity linking task of NIST KBP 2013. We show that the algorithm improves the entity linking system performance.
%U http://www.lrec-conf.org/proceedings/lrec2014/pdf/899_Paper.pdf
%P 4609-4615
Markdown (Informal)
[Improving Entity Linking using Surface Form Refinement](http://www.lrec-conf.org/proceedings/lrec2014/pdf/899_Paper.pdf) (Charton et al., LREC 2014)
ACL
- Eric Charton, Marie-Jean Meurs, Ludovic Jean-Louis, and Michel Gagnon. 2014. Improving Entity Linking using Surface Form Refinement. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), pages 4609–4615, Reykjavik, Iceland. European Language Resources Association (ELRA).