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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 3 4 Hits 1 – 20 of 70 1 Automatic Dialect Density Estimation for African American English ... Johnson, Alexander; Everson, Kevin; Ravi, Vijay. - : arXiv, 2022 BASE Show details 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 BASE Show details 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 BASE Show details 4 A Controllable Model of Grounded Response Generation ... Wu, Zeqiu; Galley, Michel; Brockett, Chris. - : arXiv, 2020 BASE Show details 5 Neural Models for Integrating Prosody in Spoken Language Understanding Tran, Trang. - 2020 BASE Show details 6 Automatic Analysis of Language Use in K-16 STEM Education and Impact on Student Performance Nadeem, Farah. - 2020 BASE Show details 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/ BASE Hide details 8 Low-Rank RNN Adaptation for Context-Aware Language Modeling Jaech, Aaron. - 2018 BASE Show details 9 Parsing Speech: A Neural Approach to Integrating Lexical and Acoustic-Prosodic Information ... Tran, Trang; Toshniwal, Shubham; Bansal, Mohit. - : arXiv, 2017 BASE Show details 10 Effective Use of Cross-Domain Parsing in Automatic Speech Recognition and Error Detection Marin, Marius. - 2015 BASE Show details 11 Automatic Characterization of Text Difficulty Medero, Julie. - 2014 BASE Show details 12 Data Selection for Statistical Machine Translation Axelrod, Amittai. - 2014 BASE Show details 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 Show details 14 Rank and Sparsity in Language Processing Hutchinson, Brian. - 2013 BASE Show details 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 BLLDB OLC Linguistik Show details 16 Graph-based Algorithms for Lexical Semantics and its Applications Wu, Wei. - 2012 BASE Show details 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 BLLDB OLC Linguistik Show details 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 Show details 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 Show details 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 BLLDB OLC Linguistik Show details Page: 1 2 3 4

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