Role-based model for Named Entity Recognition

Pablo Calleja, Raúl García-Castro, Guadalupe Aguado-de-Cea, Asunción Gómez-Pérez


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.
Anthology ID:
R17-1021
Volume:
Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017
Month:
September
Year:
2017
Address:
Varna, Bulgaria
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
149–156
Language:
URL:
https://doi.org/10.26615/978-954-452-049-6_021
DOI:
10.26615/978-954-452-049-6_021
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PDF:
https://doi.org/10.26615/978-954-452-049-6_021