{"id":"https://openalex.org/W2949550698","doi":"https://doi.org/10.18653/v1/p19-1091","title":"Joint Entity Extraction and Assertion Detection for Clinical Text","display_name":"Joint Entity Extraction and Assertion Detection for Clinical Text","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2949550698","doi":"https://doi.org/10.18653/v1/p19-1091","mag":"2949550698"},"language":"en","primary_location":{"id":"doi:10.18653/v1/p19-1091","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p19-1091","pdf_url":"https://www.aclweb.org/anthology/P19-1091.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/P19-1091.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5077875900","display_name":"Parminder Bhatia","orcid":"https://orcid.org/0000-0002-0038-5081"},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Parminder Bhatia","raw_affiliation_strings":["Amazon, USA"],"affiliations":[{"raw_affiliation_string":"Amazon, USA","institution_ids":["https://openalex.org/I1311688040"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067431228","display_name":"Busra Celikkaya","orcid":"https://orcid.org/0000-0002-7679-288X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Busra Celikkaya","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5031419933","display_name":"Mohammed Khalilia","orcid":null},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Mohammed Khalilia","raw_affiliation_strings":["Amazon, USA"],"affiliations":[{"raw_affiliation_string":"Amazon, USA","institution_ids":["https://openalex.org/I1311688040"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5031419933"],"corresponding_institution_ids":["https://openalex.org/I1311688040"],"apc_list":null,"apc_paid":null,"fwci":5.0632,"has_fulltext":true,"cited_by_count":41,"citation_normalized_percentile":{"value":0.96244556,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"954","last_page":"959"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9983000159263611,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8386538028717041},{"id":"https://openalex.org/keywords/softmax-function","display_name":"Softmax function","score":0.7684023976325989},{"id":"https://openalex.org/keywords/negation","display_name":"Negation","score":0.7342164516448975},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.6548860669136047},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6298528909683228},{"id":"https://openalex.org/keywords/named-entity-recognition","display_name":"Named-entity recognition","score":0.5824323296546936},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.551239013671875},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5510160326957703},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5087283253669739},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.43037787079811096},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.4160895347595215},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3320853114128113},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.19911053776741028},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.17552319169044495}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8386538028717041},{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.7684023976325989},{"id":"https://openalex.org/C2185349","wikidata":"https://www.wikidata.org/wiki/Q190558","display_name":"Negation","level":2,"score":0.7342164516448975},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.6548860669136047},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6298528909683228},{"id":"https://openalex.org/C2779135771","wikidata":"https://www.wikidata.org/wiki/Q403574","display_name":"Named-entity recognition","level":3,"score":0.5824323296546936},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.551239013671875},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5510160326957703},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5087283253669739},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.43037787079811096},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.4160895347595215},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3320853114128113},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.19911053776741028},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.17552319169044495},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C170154142","wikidata":"https://www.wikidata.org/wiki/Q150737","display_name":"Architectural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.18653/v1/p19-1091","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p19-1091","pdf_url":"https://www.aclweb.org/anthology/P19-1091.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1812.05270","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1812.05270","pdf_url":"https://arxiv.org/pdf/1812.05270","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.18653/v1/p19-1091","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p19-1091","pdf_url":"https://www.aclweb.org/anthology/P19-1091.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.6700000166893005,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2949550698.pdf","grobid_xml":"https://content.openalex.org/works/W2949550698.grobid-xml"},"referenced_works_count":33,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1964625659","https://openalex.org/W2016589492","https://openalex.org/W2064675550","https://openalex.org/W2104381725","https://openalex.org/W2116446440","https://openalex.org/W2131241448","https://openalex.org/W2139865360","https://openalex.org/W2143612262","https://openalex.org/W2250539671","https://openalex.org/W2250890512","https://openalex.org/W2296283641","https://openalex.org/W2308486447","https://openalex.org/W2493916176","https://openalex.org/W2508309896","https://openalex.org/W2515289471","https://openalex.org/W2532922193","https://openalex.org/W2778310824","https://openalex.org/W2794101069","https://openalex.org/W2854807801","https://openalex.org/W2903309163","https://openalex.org/W2904157398","https://openalex.org/W2917713791","https://openalex.org/W2952566282","https://openalex.org/W2963174553","https://openalex.org/W2963373823","https://openalex.org/W2963466151","https://openalex.org/W2963625095","https://openalex.org/W2963917673","https://openalex.org/W2964121744","https://openalex.org/W2968988576","https://openalex.org/W2981075286","https://openalex.org/W3007523830"],"related_works":["https://openalex.org/W3107204728","https://openalex.org/W4287591324","https://openalex.org/W3108503355","https://openalex.org/W4226420367","https://openalex.org/W2962876041","https://openalex.org/W3090555870","https://openalex.org/W3022820045","https://openalex.org/W2801655600","https://openalex.org/W3005627584","https://openalex.org/W4303493643"],"abstract_inverted_index":{"Negative":[0],"medical":[1],"findings":[2,13],"are":[3],"prevalent":[4],"in":[5],"clinical":[6,139],"reports,":[7],"yet":[8],"discriminating":[9],"them":[10],"from":[11],"positive":[12],"remains":[14],"a":[15,30,49,54,67,76,136],"challenging":[16],"task":[17,28],"for":[18,83,109,124],"information":[19],"extraction.":[20],"Most":[21],"of":[22,32,104],"the":[23,84,93,102,114,130],"existing":[24],"systems":[25],"treat":[26],"this":[27,47],"as":[29,48],"pipeline":[31],"two":[33,85],"separate":[34,81],"tasks,":[35],"i.e.,":[36],"named":[37],"entity":[38],"recognition":[39],"(NER)":[40],"and":[41,52,63,73,96,126,135],"rule-based":[42,95],"negation":[43,127],"detection.":[44],"We":[45,65],"consider":[46],"multi-task":[50],"problem":[51,103],"present":[53],"novel":[55],"end-to-end":[56],"neural":[57],"model":[58,72],"to":[59],"jointly":[60],"extract":[61],"entities":[62],"negations.":[64],"extend":[66],"standard":[68],"hierarchical":[69],"encoder-decoder":[70],"NER":[71,125],"first":[74],"adopt":[75],"shared":[77],"encoder":[78],"followed":[79],"by":[80],"decoders":[82],"tasks.":[86],"This":[87],"architecture":[88,119],"performs":[89],"considerably":[90],"better":[91],"than":[92],"previous":[94],"machine":[97],"learning-based":[98],"systems.":[99],"To":[100],"overcome":[101],"increased":[105],"parameter":[106],"size":[107],"especially":[108],"low-resource":[110],"settings,":[111],"we":[112],"propose":[113],"Conditional":[115],"Softmax":[116],"Shared":[117],"Decoder":[118],"which":[120],"achieves":[121],"state-of-art":[122],"results":[123],"detection":[128],"on":[129],"2010":[131],"i2b2/VA":[132],"challenge":[133],"dataset":[134],"proprietary":[137],"de-identified":[138],"dataset.":[140]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":11},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":11},{"year":2019,"cited_by_count":6}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
