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Hits 1 – 20 of 70
1
Automatic Dialect Density Estimation for African American English ...
Johnson, Alexander
;
Everson, Kevin
;
Ravi, Vijay
. - : arXiv, 2022
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2
DIALKI: Knowledge Identification in Conversational Systems through Dialogue-Document Contextualization ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Hajishirzi, Hannaneh
;
Lu, Bo-Ru
. - : Underline Science Inc., 2021
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3
Dialogue State Tracking with a Language Model using Schema-Driven Prompting ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Cheng, Hao
;
Lee, Chia-Hsuan
. - : Underline Science Inc., 2021
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4
A Controllable Model of Grounded Response Generation ...
Wu, Zeqiu
;
Galley, Michel
;
Brockett, Chris
. - : arXiv, 2020
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5
Neural Models for Integrating Prosody in Spoken Language Understanding
Tran, Trang
. - 2020
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6
Automatic Analysis of Language Use in K-16 STEM Education and Impact on Student Performance
Nadeem, Farah
. - 2020
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7
Asynchronous Speech Recognition Affects Physician Editing of Notes
Lybarger, Kevin J.
;
Ostendorf, Mari
;
Riskin, Eve
;
Payne, Thomas H.
;
White, Andrew A.
;
Yetisgen, Meliha
. - : Georg Thieme Verlag KG, 2018
Abstract:
Objective Clinician progress notes are an important record for care and communication, but there is a perception that electronic notes take too long to write and may not accurately reflect the patient encounter, threatening quality of care. Automatic speech recognition (ASR) has the potential to improve clinical documentation process; however, ASR inaccuracy and editing time are barriers to wider use. We hypothesized that automatic text processing technologies could decrease editing time and improve note quality. To inform the development of these technologies, we studied how physicians create clinical notes using ASR and analyzed note content that is revised or added during asynchronous editing. Materials and Methods We analyzed a corpus of 649 dictated clinical notes from 9 physicians. Notes were dictated during rounds to portable devices, automatically transcribed, and edited later at the physician's convenience. Comparing ASR transcripts and the final edited notes, we identified the word sequences edited by physicians and categorized the edits by length and content. Results We found that 40% of the words in the final notes were added by physicians while editing: 6% corresponded to short edits associated with error correction and format changes, and 34% were associated with longer edits. Short error correction edits that affect note accuracy are estimated to be less than 3% of the words in the dictated notes. Longer edits primarily involved insertion of material associated with clinical data or assessment and plans. The longer edits improve note completeness; some could be handled with verbalized commands in dictation. Conclusion Process interventions to reduce ASR documentation burden, whether related to technology or the dictation/editing workflow, should apply a portfolio of solutions to address all categories of required edits. Improved processes could reduce an important barrier to broader use of ASR by clinicians and improve note quality.
URL:
https://doi.org/10.1055/s-0038-1673417
http://www.ncbi.nlm.nih.gov/pubmed/30332689
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6192791/
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8
Low-Rank RNN Adaptation for Context-Aware Language Modeling
Jaech, Aaron
. - 2018
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9
Parsing Speech: A Neural Approach to Integrating Lexical and Acoustic-Prosodic Information ...
Tran, Trang
;
Toshniwal, Shubham
;
Bansal, Mohit
. - : arXiv, 2017
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10
Effective Use of Cross-Domain Parsing in Automatic Speech Recognition and Error Detection
Marin, Marius
. - 2015
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11
Automatic Characterization of Text Difficulty
Medero, Julie
. - 2014
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12
Data Selection for Statistical Machine Translation
Axelrod, Amittai
. - 2014
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13
Graph-based query strategies for active learning
Wu, Wei
;
Ostendorf, Mari
In:
Institute of Electrical and Electronics Engineers. IEEE transactions on audio, speech and language processing. - New York, NY : Inst.
21 (2013) 2, 260-269
OLC Linguistik
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14
Rank and Sparsity in Language Processing
Hutchinson, Brian
. - 2013
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15
Joint reranking of parsing and word recognition with automatic segmentation
Ostendorf, Mari
;
Kahn, Jeremy G.
In:
Computer speech and language. - Amsterdam [u.a.] : Elsevier
26 (2012) 1, 1-19
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16
Graph-based Algorithms for Lexical Semantics and its Applications
Wu, Wei
. - 2012
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17
Expected dependency pair match: predicting translation quality with expected syntactic structure
Ostendorf, Mari
;
Kahn, Jeremy G.
;
Snover, Matthew G.
In:
Machine translation. - Dordrecht [u.a.] : Springer Science + Business Media
23 (2010) 2-3, 169-179
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OLC Linguistik
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18
A machine learning approach to reading level assessment
Petersen, Sarah E.
;
Ostendorf, Mari
In:
Computer speech and language. - Amsterdam [u.a.] : Elsevier
23 (2009) 1, 89-106
OLC Linguistik
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19
A machine learning approach to reading level assessment
Petersen, Sarah E.
;
Ostendorf, Mari
In:
Computer speech and language. - Amsterdam [u.a.] : Elsevier
23 (2009) 1, 89-106
BLLDB
OLC Linguistik
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20
Improving robustness of MLLR adaptation with speaker-clustered regression class trees
Mandal, Arindam
;
Ostendorf, Mari
;
Stolcke, Andreas
In:
Computer speech and language. - Amsterdam [u.a.] : Elsevier
23 (2009) 2, 176-199
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OLC Linguistik
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