Deep Learning for Punctuation Restoration in Medical Reports

Salloum, Wael; Finley, Greg; Edwards, Erik; Miller, Mark A.; Suendermann-Oeft, David

doi:10.18653/v1/w17-2319

Cited by 24 publications

(11 citation statements)

References 16 publications

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“…To improve the sentence boundary classification accuracy, some work upgrade the N-gram input to variable-length input by using recurrent neural network (RNN) (Tilk and Alumäe, 2015;Salloum et al, 2017). Some other work takes punctuation restoration as a sequence labeling problem and investigates using Conditional Random Fields (CRFs) (Lu and Ng, 2010;Wang et al, 2012;Ueffing et al, 2013).…”

Section: Whole Sentence-based Methodsmentioning

confidence: 99%

Dynamic Sentence Boundary Detection for Simultaneous Translation

Zhang¹,

Zhang²

2020

Proceedings of the First Workshop on Automatic Simultaneous Translation

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Simultaneous Translation is a great challenge in which translation starts before the source sentence finished. Most studies take transcription as input and focus on balancing translation quality and latency for each sentence. However, most ASR systems can not provide accurate sentence boundaries in realtime. Thus it is a key problem to segment sentences for the word streaming before translation. In this paper, we propose a novel method for sentence boundary detection that takes it as a multi-class classification task under the endto-end pre-training framework. Experiments show significant improvements both in terms of translation quality and latency.

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Section: Whole Sentence-based Methodsmentioning

confidence: 99%

Dynamic Sentence Boundary Detection for Simultaneous Translation

Zhang¹,

Zhang²

2020

Proceedings of the First Workshop on Automatic Simultaneous Translation

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“…In the last couple of years, the work in biomedical NLP was dominated by applications of deep learning to: punctuation restoration [68], text classification [69], relation extraction [70] [71] [72] [73], information retrieval [74], and similarity judgments [75], among other exciting progress in biomedical language processing. For a more detailed exploration of recent topics, the BioNLP Annual Workshop [76] covers the most researched and debatable areas.…”

Section: Future Workmentioning

confidence: 99%

Biomedical Term Extraction: NLP Techniques in Computational Medicine

Sandoval¹,

Díaz²,

Llanos³

et al. 2019

IJIMAI

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Artificial Intelligence (AI) and its branch Natural Language Processing (NLP) in particular are main contributors to recent advances in classifying documentation and extracting information from assorted fields, Medicine being one that has gathered a lot of attention due to the amount of information generated in public professional journals and other means of communication within the medical profession. The typical information extraction task from technical texts is performed via an automatic term recognition extractor. Automatic Term Recognition (ATR) from technical texts is applied for the identification of key concepts for information retrieval and, secondarily, for machine translation. Term recognition depends on the subject domain and the lexical patterns of a given language, in our case, Spanish, Arabic and Japanese. In this article, we present the methods and techniques for creating a biomedical corpus of validated terms, with several tools for optimal exploitation of the information therewith contained in said corpus. This paper also shows how these techniques and tools have been used in a prototype.

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“…To date, research effort has focused on solving foundational problems in the development of a digital scribe, including ASR of medical conversations, 10,11 automatically populating the review of symptoms discussed in a medical encounter, 12 extracting symptoms from medical conversations, 13,14 and generating medical reports from dictations. 15,16 While these developments are promising, several challenges hinder the implementation of a fully functioning digital scribe and its evaluation in a clinical environment. This paper will discuss the major challenges, with a summary presented in Table 1.…”

Section: Introductionmentioning

confidence: 99%

Challenges of developing a digital scribe to reduce clinical documentation burden

et al. 2019

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Clinicians spend a large amount of time on clinical documentation of patient encounters, often impacting quality of care and clinician satisfaction, and causing physician burnout. Advances in artificial intelligence (AI) and machine learning (ML) open the possibility of automating clinical documentation with digital scribes, using speech recognition to eliminate manual documentation by clinicians or medical scribes. However, developing a digital scribe is fraught with problems due to the complex nature of clinical environments and clinical conversations. This paper identifies and discusses major challenges associated with developing automated speech-based documentation in clinical settings: recording high-quality audio, converting audio to transcripts using speech recognition, inducing topic structure from conversation data, extracting medical concepts, generating clinically meaningful summaries of conversations, and obtaining clinical data for AI and ML algorithms.npj Digital Medicine (2019) 2:114 ; https://doi.

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Deep Learning for Punctuation Restoration in Medical Reports

Cited by 24 publications

References 16 publications

Dynamic Sentence Boundary Detection for Simultaneous Translation

Dynamic Sentence Boundary Detection for Simultaneous Translation

Biomedical Term Extraction: NLP Techniques in Computational Medicine

Challenges of developing a digital scribe to reduce clinical documentation burden

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