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    <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.</abstract>
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%0 Conference Proceedings
%T Deep Learning for Punctuation Restoration in Medical Reports
%A Salloum, Wael
%A Finley, Greg
%A Edwards, Erik
%A Miller, Mark
%A Suendermann-Oeft, David
%Y Cohen, Kevin Bretonnel
%Y Demner-Fushman, Dina
%Y Ananiadou, Sophia
%Y Tsujii, Junichi
%S BioNLP 2017
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada,
%F salloum-etal-2017-deep
%X 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.
%R 10.18653/v1/W17-2319
%U https://aclanthology.org/W17-2319
%U https://doi.org/10.18653/v1/W17-2319
%P 159-164

Markdown (Informal)

[Deep Learning for Punctuation Restoration in Medical Reports](https://aclanthology.org/W17-2319) (Salloum et al., BioNLP 2017)

• Deep Learning for Punctuation Restoration in Medical Reports (Salloum et al., BioNLP 2017)

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.