@inproceedings{lopez-etal-2014-generating,
title = "Generating a Resource for Products and Brandnames Recognition. Application to the Cosmetic Domain.",
author = "Lopez, C{\'e}dric and
Segond, Fr{\'e}d{\'e}rique and
Hondermarck, Olivier and
Curtoni, Paolo and
Dini, Luca",
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/549_Paper.pdf",
pages = "2559--2564",
abstract = "Named Entity Recognition task needs high-quality and large-scale resources. In this paper, we present RENCO, a based-rules system focused on the recognition of entities in the Cosmetic domain (brandnames, product names, {\^a}¦). RENCO has two main objectives: 1) Generating resources for named entity recognition; 2) Mining new named entities relying on the previous generated resources. In order to build lexical resources for the cosmetic domain, we propose a system based on local lexico-syntactic rules complemented by a learning module. As the outcome of the system, we generate both a simple lexicon and a structured lexicon. Results of the evaluation show that even if RENCO outperforms a classic Conditional Random Fields algorithm, both systems should combine their respective strengths.",
}
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<abstract>Named Entity Recognition task needs high-quality and large-scale resources. In this paper, we present RENCO, a based-rules system focused on the recognition of entities in the Cosmetic domain (brandnames, product names, â¦). RENCO has two main objectives: 1) Generating resources for named entity recognition; 2) Mining new named entities relying on the previous generated resources. In order to build lexical resources for the cosmetic domain, we propose a system based on local lexico-syntactic rules complemented by a learning module. As the outcome of the system, we generate both a simple lexicon and a structured lexicon. Results of the evaluation show that even if RENCO outperforms a classic Conditional Random Fields algorithm, both systems should combine their respective strengths.</abstract>
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%0 Conference Proceedings
%T Generating a Resource for Products and Brandnames Recognition. Application to the Cosmetic Domain.
%A Lopez, Cédric
%A Segond, Frédérique
%A Hondermarck, Olivier
%A Curtoni, Paolo
%A Dini, Luca
%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 lopez-etal-2014-generating
%X Named Entity Recognition task needs high-quality and large-scale resources. In this paper, we present RENCO, a based-rules system focused on the recognition of entities in the Cosmetic domain (brandnames, product names, â¦). RENCO has two main objectives: 1) Generating resources for named entity recognition; 2) Mining new named entities relying on the previous generated resources. In order to build lexical resources for the cosmetic domain, we propose a system based on local lexico-syntactic rules complemented by a learning module. As the outcome of the system, we generate both a simple lexicon and a structured lexicon. Results of the evaluation show that even if RENCO outperforms a classic Conditional Random Fields algorithm, both systems should combine their respective strengths.
%U http://www.lrec-conf.org/proceedings/lrec2014/pdf/549_Paper.pdf
%P 2559-2564
Markdown (Informal)
[Generating a Resource for Products and Brandnames Recognition. Application to the Cosmetic Domain.](http://www.lrec-conf.org/proceedings/lrec2014/pdf/549_Paper.pdf) (Lopez et al., LREC 2014)
ACL