Abstract
We propose an approach for named entity recognition in medical data, using a character-based deep bidirectional recurrent neural network. Such models can learn features and patterns based on the character sequence, and are not limited to a fixed vocabulary. This makes them very well suited for the NER task in the medical domain. Our experimental evaluation shows promising results, with a 60% improvement in F 1 score over the baseline, and our system generalizes well between different datasets.- Anthology ID:
- W16-5104
- Volume:
- Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining (BioTxtM2016)
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Editors:
- Sophia Ananiadou, Riza Batista-Navarro, Kevin Bretonnel Cohen, Dina Demner-Fushman, Paul Thompson
- Venue:
- WS
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 30–39
- Language:
- URL:
- https://aclanthology.org/W16-5104
- DOI:
- Cite (ACL):
- Simon Almgren, Sean Pavlov, and Olof Mogren. 2016. Named Entity Recognition in Swedish Health Records with Character-Based Deep Bidirectional LSTMs. In Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining (BioTxtM2016), pages 30–39, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- Named Entity Recognition in Swedish Health Records with Character-Based Deep Bidirectional LSTMs (Almgren et al., 2016)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/W16-5104.pdf
- Code
- olofmogren/biomedical-ner-data-swedish