{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T10:18:28Z","timestamp":1773051508803,"version":"3.50.1"},"reference-count":56,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62102316"],"award-info":[{"award-number":["62102316"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62171382"],"award-info":[{"award-number":["62171382"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Common Information Systems Fund","award":["315197202"],"award-info":[{"award-number":["315197202"]}]},{"DOI":"10.13039\/501100018594","name":"Central University Basic Research Fund of China","doi-asserted-by":"publisher","award":["G2021KY05114"],"award-info":[{"award-number":["G2021KY05114"]}],"id":[{"id":"10.13039\/501100018594","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012130","name":"Chinese Aeronautical Establishment","doi-asserted-by":"publisher","award":["20200051053002"],"award-info":[{"award-number":["20200051053002"]}],"id":[{"id":"10.13039\/501100012130","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Rel."],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1109\/tr.2023.3336330","type":"journal-article","created":{"date-parts":[[2023,12,8]],"date-time":"2023-12-08T19:14:44Z","timestamp":1702062884000},"page":"1280-1290","source":"Crossref","is-referenced-by-count":72,"title":["LI-EMRSQL: Linking Information Enhanced Text2SQL Parsing on Complex Electronic Medical Records"],"prefix":"10.1109","volume":"73","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6350-0502","authenticated-orcid":false,"given":"Qing","family":"Li","sequence":"first","affiliation":[{"name":"School of Computer Science, Northwestern Polytechnical University, Xi&#x0027;an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0023-7617","authenticated-orcid":false,"given":"Tao","family":"You","sequence":"additional","affiliation":[{"name":"School of Computer Science, Northwestern Polytechnical University, Xi&#x0027;an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6234-1001","authenticated-orcid":false,"given":"Jinchao","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science, Northwestern Polytechnical University, Xi&#x0027;an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7557-2965","authenticated-orcid":false,"given":"Ying","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Northwestern Polytechnical University, Xi&#x0027;an, China"}]},{"given":"Chenglie","family":"Du","sequence":"additional","affiliation":[{"name":"School of Computer Science, Northwestern Polytechnical University, Xi&#x0027;an, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2016.35"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2018.178"},{"key":"ref3","first-page":"351","article-title":"Improving text-to-SQL evaluation methodology","volume-title":"Proc. 56th Annu. Meeting Assoc. Comput. Linguistics","author":"Finegan-Dollak","year":"2018"},{"key":"ref4","first-page":"963","article-title":"Learning a neural semantic parser from user feedback","volume-title":"Proc. 55th Annu. Meeting Assoc. Comput. Linguistics","author":"Iyer","year":"2017"},{"key":"ref5","article-title":"IncSQL: Training incremental text-to-SQL parsers with non-deterministic oracles","author":"Shi","year":"2018"},{"key":"ref6","first-page":"731","article-title":"Coarse-to-fine decoding for neural semantic parsing","volume-title":"Proc. 56th Annu. Meeting Assoc. Comput. Linguistics","author":"Dong","year":"2018"},{"key":"ref7","article-title":"SQLNet: Generating structured queries from natural language without reinforcement learning","author":"Xu","year":"2017"},{"key":"ref8","article-title":"Seq2SQL: Generating structured queries from natural language using reinforcement learning","author":"Zhong","year":"2017"},{"key":"ref9","first-page":"3911","article-title":"Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-SQL task","volume-title":"Proc. Conf. Empirical Methods Natural Lang. Process.","author":"Yu","year":"2018"},{"key":"ref10","doi-asserted-by":"crossref","first-page":"7567","DOI":"10.18653\/v1\/2020.acl-main.677","article-title":"RAT-SQL: Relation-aware schema encoding and linking for text-to-SQL parsers","volume-title":"Proc. 58th Annu. Meeting Assoc. Comput. Linguistics","author":"Wang","year":"2020"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-023-01898-3"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380120"},{"key":"ref13","article-title":"EhrSQL: A practical text-to-SQL benchmark for electronic health records","author":"Lee"},{"key":"ref14","first-page":"6943","article-title":"Re-examining the role of schema linking in text-to-SQL","volume-title":"Proc. Conf. Empirical Methods Natural Lang. Process.","author":"Lei","year":"2020"},{"key":"ref15","first-page":"366","article-title":"Meta-learning for domain generalization in semantic parsing","volume-title":"Proc. Conf. North Amer. Chapter Assoc. Comput. Linguistics: Hum. Lang. Technol.","author":"Wang","year":"2021"},{"key":"ref16","first-page":"2541","article-title":"LGESQL: Line graph enhanced text-to-SQL model with mixed local and non-local relations","volume-title":"Proc. 59th Annu. Meeting Assoc. Comput. Linguistics 11th Int. Joint Conf. Natural Lang. Process.","author":"Cao","year":"2021"},{"key":"ref17","first-page":"7664","article-title":"SADGA: Structure-aware dual graph aggregation network for text-to-SQL","volume":"34","author":"Cai","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref18","first-page":"3659","article-title":"Global reasoning over database structures for text-to-SQL parsing","volume-title":"Proc. Conf. Empirical Methods Natural Lang. Process. 9th Int. Joint Conf. Natural Lang. Process.","author":"Bogin","year":"2019"},{"key":"ref19","first-page":"4870","article-title":"Bridging textual and tabular data for cross-domain text-to-SQL semantic parsing","volume-title":"Findings Assoc. Comput. Linguistics","author":"Lin","year":"2020"},{"key":"ref20","first-page":"5567","article-title":"GNN: Graph projection neural network for text-to-SQL parser","volume-title":"Proc. Conf. North Amer. Chapter Assoc. Comput. Linguistics: Hum. Lang. Technol.","author":"Chen","year":"2021"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i14.17550"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3532069"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i10.21320"},{"key":"ref24","first-page":"4171","article-title":"BERT:Pre-training of deep bidirectional transformers for language understanding","volume-title":"Proc. Conf. North Amer. Chapter Assoc. Comput. Linguistics: Hum. Lang. Technol.","author":"Devlin","year":"2019"},{"key":"ref25","doi-asserted-by":"crossref","first-page":"4320","DOI":"10.18653\/v1\/2020.acl-main.398","article-title":"TaPas: Weakly supervised table parsing via pre-training","volume-title":"Proc. 58th Annu. Meeting Assoc. Comput. Linguistics","author":"Herzig","year":"2020"},{"key":"ref26","doi-asserted-by":"crossref","first-page":"8413","DOI":"10.18653\/v1\/2020.acl-main.745","article-title":"TaBERT: Pretraining for joint understanding of textual and tabular data","volume-title":"Proc. 58th Annu. Meeting Assoc. Comput. Linguistics","author":"Yin","year":"2020"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2016.2598561"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098036"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380297"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/190"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE51399.2021.00084"},{"key":"ref32","article-title":"GraPPa: Grammar-augmented pre-training for table semantic parsing","author":"Yu","year":"2021","journal-title":"Proc. 9th Int. Conf. Learn. Representations"},{"key":"ref33","first-page":"1337","article-title":"Structure-grounded pretraining for text-to-SQL","volume-title":"Proc. Conf. North Amer. Chapter Assoc. Comput. Linguistics: Hum. Lang. Technol.","author":"Deng","year":"2021"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i15.17627"},{"key":"ref35","first-page":"36","article-title":"Knowledge graph-based question answering with electronic health records","volume-title":"Proc. Mach. Learn. Healthcare Conf.","author":"Park","year":"2021"},{"key":"ref36","first-page":"1551","article-title":"CliCR: A dataset of clinical case reports for machine reading comprehension","volume-title":"Proc. Conf. North Amer. Chapter Assoc. Comput. Linguistics: Hum. Lang. Technol.","author":"uster","year":"2018"},{"key":"ref37","first-page":"2357","article-title":"emrQA: A large corpus for question answering on electronic medical records","volume-title":"Proc. Conf. Empirical Methods Natural Lang. Process.","author":"Pampari","year":"2018"},{"key":"ref38","first-page":"3840","article-title":"Question answering with long multiple-span answers","volume-title":"Proc. Findings Assoc. Computat. Linguistics: EMNLP","author":"Zhu","year":"2020"},{"key":"ref39","doi-asserted-by":"crossref","first-page":"64","DOI":"10.18653\/v1\/2021.bionlp-1.7","article-title":"emrKBQA: A clinical knowledge-base question answering dataset","volume-title":"Proc. 20th Workshop Biomed. Lang. Process.","author":"Raghavan","year":"2021"},{"key":"ref40","first-page":"440","article-title":"A syntactic neural model for general-purpose code generation","volume-title":"Proc. 55th Annu. Meeting Assoc. Comput. Linguistics","author":"Yin","year":"2017"},{"key":"ref41","first-page":"6338","article-title":"Poincar embeddings for learning hierarchical representations","volume-title":"Proc. Adv. Neural Inf. Process. Syst: Annu. Conf. Neural Inf. Process. Syst.","author":"Nickel","year":"2017"},{"key":"ref42","article-title":"Poincar glove: Hyperbolic word embeddings","author":"Tifrea","year":"2018"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1145\/604045.604070"},{"key":"ref45","first-page":"1050","article-title":"Learning to parse database queries using inductive logic programming","volume-title":"Proc. Nat. Conf. Artif. Intell.","author":"Zelle","year":"1996"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.14778\/2735461.2735468"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1145\/3133887"},{"key":"ref48","first-page":"8024","article-title":"Pytorch: An imperative style, high-performance deep learning library","volume-title":"Proc. Adv. Neural Inf. Process: Annu. Conf. Neural Inf. Process. Syst.","author":"Paszke","year":"2019"},{"key":"ref49","article-title":"ELECTRA: Pre-training text encoders as discriminators rather than generators","volume":"85","author":"Clark","year":"2016","journal-title":"Electra"},{"key":"ref50","first-page":"1532","article-title":"GloVe: Global vectors for word representation","volume-title":"Proc. Conf. Empirical Methods Natural Lang. Process.","author":"Pennington","year":"2014"},{"key":"ref51","first-page":"249","article-title":"Understanding the difficulty of training deep feedforward neural networks","volume-title":"Proc. 13th Int. Conf. Artif. Intell. Statist., JMLR Workshop Conf. Proc.","author":"Glorot","year":"2010"},{"key":"ref52","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014","journal-title":"Comput. Sci."},{"issue":"1","key":"ref53","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref54","article-title":"Decoupled weight decay regularization","volume-title":"Proc. 7th Int. Conf. Learn. Representations","author":"Loshchilov","year":"2019"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i11.26535"},{"key":"ref56","first-page":"2030","article-title":"Natural SQL: Making SQL easier to infer from natural language specifications","volume-title":"Proc. Findings Assoc. Comput. Linguistics","author":"Gan","year":"2021"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-long.195"}],"container-title":["IEEE Transactions on Reliability"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/24\/10547161\/10351031.pdf?arnumber=10351031","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,11]],"date-time":"2024-12-11T01:01:06Z","timestamp":1733878866000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10351031\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6]]},"references-count":56,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/tr.2023.3336330","relation":{},"ISSN":["0018-9529","1558-1721"],"issn-type":[{"value":"0018-9529","type":"print"},{"value":"1558-1721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6]]}}}