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
- Editors:
- Ruslan Mitkov, Galia Angelova
- 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
- Cite (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..
- Cite (Informal):
- Role-based model for Named Entity Recognition (Calleja et al., RANLP 2017)
- PDF:
- https://doi.org/10.26615/978-954-452-049-6_021