@inproceedings{calleja-etal-2017-role,
title = "Role-based model for Named Entity Recognition",
author = "Calleja, Pablo and
Garc{\'\i}a-Castro, Ra{\'u}l and
Aguado-de-Cea, Guadalupe and
G{\'o}mez-P{\'e}rez, Asunci{\'o}n",
editor = "Mitkov, Ruslan and
Angelova, Galia",
booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
month = sep,
year = "2017",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd.",
url = "https://doi.org/10.26615/978-954-452-049-6_021",
doi = "10.26615/978-954-452-049-6_021",
pages = "149--156",
abstract = "Named Entity Recognition (NER) poses new challenges in real-world documents in which there are entities with different roles according to their purpose or meaning. Retrieving all the possible entities in scenarios in which only a subset of them based on their role is needed, produces noise on the overall precision. This work proposes a NER model that relies on role classification models that support recognizing entities with a specific role. The proposed model has been implemented in two use cases using Spanish drug Summary of Product Characteristics: identification of therapeutic indications and identification of adverse reactions. The results show how precision is increased using a NER model that is oriented towards a specific role and discards entities out of scope.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="calleja-etal-2017-role">
<titleInfo>
<title>Role-based model for Named Entity Recognition</title>
</titleInfo>
<name type="personal">
<namePart type="given">Pablo</namePart>
<namePart type="family">Calleja</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Raúl</namePart>
<namePart type="family">García-Castro</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Guadalupe</namePart>
<namePart type="family">Aguado-de-Cea</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Asunción</namePart>
<namePart type="family">Gómez-Pérez</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2017-09</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017</title>
</titleInfo>
<name type="personal">
<namePart type="given">Ruslan</namePart>
<namePart type="family">Mitkov</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Galia</namePart>
<namePart type="family">Angelova</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>INCOMA Ltd.</publisher>
<place>
<placeTerm type="text">Varna, Bulgaria</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Named Entity Recognition (NER) poses new challenges in real-world documents in which there are entities with different roles according to their purpose or meaning. Retrieving all the possible entities in scenarios in which only a subset of them based on their role is needed, produces noise on the overall precision. This work proposes a NER model that relies on role classification models that support recognizing entities with a specific role. The proposed model has been implemented in two use cases using Spanish drug Summary of Product Characteristics: identification of therapeutic indications and identification of adverse reactions. The results show how precision is increased using a NER model that is oriented towards a specific role and discards entities out of scope.</abstract>
<identifier type="citekey">calleja-etal-2017-role</identifier>
<identifier type="doi">10.26615/978-954-452-049-6_021</identifier>
<part>
<date>2017-09</date>
<extent unit="page">
<start>149</start>
<end>156</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Role-based model for Named Entity Recognition
%A Calleja, Pablo
%A García-Castro, Raúl
%A Aguado-de-Cea, Guadalupe
%A Gómez-Pérez, Asunción
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017
%D 2017
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F calleja-etal-2017-role
%X Named Entity Recognition (NER) poses new challenges in real-world documents in which there are entities with different roles according to their purpose or meaning. Retrieving all the possible entities in scenarios in which only a subset of them based on their role is needed, produces noise on the overall precision. This work proposes a NER model that relies on role classification models that support recognizing entities with a specific role. The proposed model has been implemented in two use cases using Spanish drug Summary of Product Characteristics: identification of therapeutic indications and identification of adverse reactions. The results show how precision is increased using a NER model that is oriented towards a specific role and discards entities out of scope.
%R 10.26615/978-954-452-049-6_021
%U https://doi.org/10.26615/978-954-452-049-6_021
%P 149-156
Markdown (Informal)
[Role-based model for Named Entity Recognition](https://doi.org/10.26615/978-954-452-049-6_021) (Calleja et al., RANLP 2017)
ACL
- Pablo Calleja, Raúl García-Castro, Guadalupe Aguado-de-Cea, and Asunción Gómez-Pérez. 2017. Role-based model for Named Entity Recognition. In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pages 149–156, Varna, Bulgaria. INCOMA Ltd..