RNN Embeddings for Identifying Difficult to Understand Medical Words
Hanna Pylieva, Artem Chernodub, Natalia Grabar, Thierry Hamon
Abstract
Patients and their families often require a better understanding of medical information provided by doctors. We currently address this issue by improving the identification of difficult to understand medical words. We introduce novel embeddings received from RNN - FrnnMUTE (French RNN Medical Understandability Text Embeddings) which allow to reach up to 87.0 F1 score in identification of difficult words. We also note that adding pre-trained FastText word embeddings to the feature set substantially improves the performance of the model which classifies words according to their difficulty. We study the generalizability of different models through three cross-validation scenarios which allow testing classifiers in real-world conditions: understanding of medical words by new users, and classification of new unseen words by the automatic models. The RNN - FrnnMUTE embeddings and the categorization code are being made available for the research.- Anthology ID:
- W19-5011
- Volume:
- Proceedings of the 18th BioNLP Workshop and Shared Task
- Month:
- August
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Dina Demner-Fushman, Kevin Bretonnel Cohen, Sophia Ananiadou, Junichi Tsujii
- Venue:
- BioNLP
- SIG:
- SIGBIOMED
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 97–104
- Language:
- URL:
- https://preview.aclanthology.org/build-pipeline-with-new-library/W19-5011/
- DOI:
- 10.18653/v1/W19-5011
- Cite (ACL):
- Hanna Pylieva, Artem Chernodub, Natalia Grabar, and Thierry Hamon. 2019. RNN Embeddings for Identifying Difficult to Understand Medical Words. In Proceedings of the 18th BioNLP Workshop and Shared Task, pages 97–104, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- RNN Embeddings for Identifying Difficult to Understand Medical Words (Pylieva et al., BioNLP 2019)
- PDF:
- https://preview.aclanthology.org/build-pipeline-with-new-library/W19-5011.pdf
- Code
- hpylieva/FrnnMUTE