2026-09-21T03:18:59Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/102500 2023-07-11 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

Peng, Jian Pampari, Anusri 2019-02-06T19:36:41Z 2019-02-06T19:36:41Z 2018-12-11 2018-12 We propose a novel methodology to generate domain-specific large-scale question answering (QA) datasets by re-purposing existing annotations for other NLP tasks. We demonstrate an instance of this methodology in generating a large-scale QA dataset for electronic medical records by leveraging existing expert annotations on clinical notes for various NLP tasks from the community shared i2b2 datasets. The resulting corpus (emrQA) has 1 million question-logical form and 400,000+ question-answer evidence pairs. We characterize the dataset and explore its learning potential by training baseline models for question to logical form and question to answer mapping. Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2019-02-05 without embargo terms The student, Anusri Pampari, accepted the attached license on 2018-12-10 at 17:04. The student, Anusri Pampari, submitted this Thesis for approval on 2018-12-10 at 17:14. This Thesis was approved for publication on 2018-12-11 at 16:32. DSpace SAF Submission Ingestion Package generated from Vireo submission #13258 on 2019-02-05 at 11:15:51 Made available in DSpace on 2019-02-06T19:36:41Z (GMT). No. of bitstreams: 2 PAMPARI-THESIS-2018.pdf: 647132 bytes, checksum: 7f5c13ac50da2e60244c6e0cc573fc40 (MD5) LICENSE.txt: 4211 bytes, checksum: 766b4aeffc00e3dd1bd91d2b58ec3638 (MD5) Previous issue date: 2018-12-11 application/pdf http://hdl.handle.net/2142/102500 en Accepted at Conference on Empirical Methods in Natural Language Processing (EMNLP) 2018 Electronic Medical Records, Question Answering, Logical Forms, Semantic Parsing, Dataset Generation, Closed Domain, i2b2 emrQA: A large corpus for question answering on electronic medical records text text Computer Science Computer Science University of Illinois at Urbana-Champaign Thesis M.S.