{"id":"https://openalex.org/W4206947710","doi":"https://doi.org/10.1145/3502223.3502228","title":"ROSE-NER: Robust Semi-supervised Named Entity Recognition on Insufficient Labeled Data","display_name":"ROSE-NER: Robust Semi-supervised Named Entity Recognition on Insufficient Labeled Data","publication_year":2021,"publication_date":"2021-12-06","ids":{"openalex":"https://openalex.org/W4206947710","doi":"https://doi.org/10.1145/3502223.3502228"},"language":"en","primary_location":{"id":"doi:10.1145/3502223.3502228","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3502223.3502228","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 10th International Joint Conference on Knowledge Graphs","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101920994","display_name":"Haiyan Chen","orcid":"https://orcid.org/0009-0005-1640-4442"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Haiyan Chen","raw_affiliation_strings":["Southeast University, China"],"affiliations":[{"raw_affiliation_string":"Southeast University, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067201023","display_name":"Shuwei Yuan","orcid":null},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuwei Yuan","raw_affiliation_strings":["Southeast University, China"],"affiliations":[{"raw_affiliation_string":"Southeast University, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100368882","display_name":"Xiang Zhang","orcid":"https://orcid.org/0000-0002-7408-4070"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiang Zhang","raw_affiliation_strings":["Southeast University, China"],"affiliations":[{"raw_affiliation_string":"Southeast University, China","institution_ids":["https://openalex.org/I76569877"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101920994"],"corresponding_institution_ids":["https://openalex.org/I76569877"],"apc_list":null,"apc_paid":null,"fwci":0.5439,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.73890096,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9998000264167786,"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":0.9998000264167786,"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":0.995199978351593,"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/T11719","display_name":"Data Quality and Management","score":0.9901999831199646,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/named-entity-recognition","display_name":"Named-entity recognition","score":0.8235124349594116},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.8156981468200684},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7811785936355591},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.7750522494316101},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6301482915878296},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5554001331329346},{"id":"https://openalex.org/keywords/dependency","display_name":"Dependency (UML)","score":0.4777109622955322},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4754369258880615},{"id":"https://openalex.org/keywords/noisy-data","display_name":"Noisy data","score":0.4669937193393707},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.46169719099998474},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4250043034553528},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3452717065811157},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.32636725902557373},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.0845412015914917}],"concepts":[{"id":"https://openalex.org/C2779135771","wikidata":"https://www.wikidata.org/wiki/Q403574","display_name":"Named-entity recognition","level":3,"score":0.8235124349594116},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.8156981468200684},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7811785936355591},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.7750522494316101},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6301482915878296},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5554001331329346},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.4777109622955322},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4754369258880615},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.4669937193393707},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46169719099998474},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4250043034553528},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3452717065811157},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32636725902557373},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0845412015914917},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3502223.3502228","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3502223.3502228","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 10th International Joint Conference on Knowledge Graphs","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W2141099517","https://openalex.org/W2142016317","https://openalex.org/W2169099542","https://openalex.org/W2414378847","https://openalex.org/W2602856279","https://openalex.org/W2604019706","https://openalex.org/W2734693922","https://openalex.org/W2799448136","https://openalex.org/W2888041867","https://openalex.org/W2891383691","https://openalex.org/W2912102336","https://openalex.org/W2945878859","https://openalex.org/W2949204161","https://openalex.org/W2963140597","https://openalex.org/W2963351448","https://openalex.org/W2963558486","https://openalex.org/W3038098779","https://openalex.org/W3102903864","https://openalex.org/W3103981637","https://openalex.org/W3105011057","https://openalex.org/W4205918858"],"related_works":["https://openalex.org/W2997340383","https://openalex.org/W3139213884","https://openalex.org/W4317548404","https://openalex.org/W3022007134","https://openalex.org/W2130553454","https://openalex.org/W160673860","https://openalex.org/W2033364610","https://openalex.org/W2797776314","https://openalex.org/W3163689946","https://openalex.org/W4308757581"],"abstract_inverted_index":{"With":[0],"the":[1,11,45,55,105,118,121,125],"wide":[2],"application":[3],"of":[4,13,33,47,57,85,92,98,120,127],"deep":[5],"learning":[6],"technology":[7],"in":[8,39,44,104],"recent":[9],"years,":[10],"effect":[12],"named":[14],"entity":[15],"recognition":[16],"tasks":[17],"has":[18],"been":[19],"significantly":[20],"improved":[21],"when":[22],"there":[23],"is":[24,36,79],"sufficient":[25],"labeled":[26,34,86,142],"data.":[27,95],"However,":[28],"obtaining":[29],"a":[30,65,83,89],"large":[31,90],"amount":[32,91],"data":[35,50,87,100],"quite":[37],"difficult":[38],"many":[40],"domains,":[41],"for":[42],"example":[43],"domain":[46],"medicine.":[48],"Noisy":[49],"can":[51],"also":[52],"negatively":[53],"affect":[54],"robustness":[56,119],"NER":[58,68],"models.":[59],"In":[60,108],"this":[61],"paper,":[62],"we":[63,110],"propose":[64],"robust":[66],"semi-supervised":[67,77],"approach":[69,137],"ROSE-NER":[70],"to":[71,81,116],"tackle":[72],"these":[73,99],"challenges.":[74],"A":[75],"two-step":[76],"model":[78,106,122],"introduced":[80],"expand":[82],"handful":[84],"with":[88],"predicted":[93],"pseudo-labeled":[94],"The":[96],"combination":[97],"will":[101],"be":[102],"exploited":[103],"training.":[107],"addition,":[109],"introduce":[111],"an":[112],"adversarial":[113],"training":[114],"method":[115],"improve":[117],"by":[123],"eliminating":[124],"impact":[126],"noise":[128],"samples.":[129],"Experiments":[130],"on":[131,140],"medical":[132],"datasets":[133],"show":[134],"that":[135],"our":[136],"reduces":[138],"dependency":[139],"massive":[141],"data,":[143],"and":[144],"it":[145],"outperforms":[146],"other":[147],"State-of-the-Art":[148],"approaches.":[149]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}