Deep Learning for Punctuation Restoration in Medical Reports
Wael Salloum, Greg Finley, Erik Edwards, Mark Miller, David Suendermann-Oeft
Abstract
In clinical dictation, speakers try to be as concise as possible to save time, often resulting in utterances without explicit punctuation commands. Since the end product of a dictated report, e.g. an out-patient letter, does require correct orthography, including exact punctuation, the latter need to be restored, preferably by automated means. This paper describes a method for punctuation restoration based on a state-of-the-art stack of NLP and machine learning techniques including B-RNNs with an attention mechanism and late fusion, as well as a feature extraction technique tailored to the processing of medical terminology using a novel vocabulary reduction model. To the best of our knowledge, the resulting performance is superior to that reported in prior art on similar tasks.- Anthology ID:
- W17-2319
- Volume:
- BioNLP 2017
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada,
- Editors:
- Kevin Bretonnel Cohen, Dina Demner-Fushman, Sophia Ananiadou, Junichi Tsujii
- Venue:
- BioNLP
- SIG:
- SIGBIOMED
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 159–164
- Language:
- URL:
- https://aclanthology.org/W17-2319
- DOI:
- 10.18653/v1/W17-2319
- Cite (ACL):
- Wael Salloum, Greg Finley, Erik Edwards, Mark Miller, and David Suendermann-Oeft. 2017. Deep Learning for Punctuation Restoration in Medical Reports. In BioNLP 2017, pages 159–164, Vancouver, Canada,. Association for Computational Linguistics.
- Cite (Informal):
- Deep Learning for Punctuation Restoration in Medical Reports (Salloum et al., BioNLP 2017)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/W17-2319.pdf