{"id":"https://openalex.org/W7132843742","doi":"https://doi.org/10.1145/3799423","title":"Enhancing Low-Resource Joint Entity and Relation Extraction Using Large Language Models within Semi-Supervised Learning","display_name":"Enhancing Low-Resource Joint Entity and Relation Extraction Using Large Language Models within Semi-Supervised Learning","publication_year":2026,"publication_date":"2026-02-28","ids":{"openalex":"https://openalex.org/W7132843742","doi":"https://doi.org/10.1145/3799423"},"language":"en","primary_location":{"id":"doi:10.1145/3799423","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3799423","pdf_url":null,"source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Knowledge Discovery from Data","raw_type":"journal-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":null,"display_name":"Xiaoqi Wang","orcid":"https://orcid.org/0009-0008-3683-3959"},"institutions":[{"id":"https://openalex.org/I134687103","display_name":"Nanjing Tech University","ror":"https://ror.org/03sd35x91","country_code":"CN","type":"education","lineage":["https://openalex.org/I134687103"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoqi Wang","raw_affiliation_strings":["College of Computer and Information Sciences, Nanjing Tech University, Nanjing, China"],"raw_orcid":"https://orcid.org/0009-0008-3683-3959","affiliations":[{"raw_affiliation_string":"College of Computer and Information Sciences, Nanjing Tech University, Nanjing, China","institution_ids":["https://openalex.org/I134687103"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127622538","display_name":"Hang Shen","orcid":null},"institutions":[{"id":"https://openalex.org/I134687103","display_name":"Nanjing Tech University","ror":"https://ror.org/03sd35x91","country_code":"CN","type":"education","lineage":["https://openalex.org/I134687103"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hang Shen","raw_affiliation_strings":["College of Computer and Information Sciences, Nanjing Tech University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-8804-2787","affiliations":[{"raw_affiliation_string":"College of Computer and Information Sciences, Nanjing Tech University, Nanjing, China","institution_ids":["https://openalex.org/I134687103"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127546526","display_name":"Honglei Qi","orcid":null},"institutions":[{"id":"https://openalex.org/I196934937","display_name":"Liaocheng University","ror":"https://ror.org/03yh0n709","country_code":"CN","type":"education","lineage":["https://openalex.org/I196934937"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Honglei Qi","raw_affiliation_strings":["Zhongtong Bus Co., Ltd., Liaocheng, China"],"raw_orcid":"https://orcid.org/0009-0002-3564-6023","affiliations":[{"raw_affiliation_string":"Zhongtong Bus Co., Ltd., Liaocheng, China","institution_ids":["https://openalex.org/I196934937"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5067766254","display_name":"Yuanfei Dai","orcid":"https://orcid.org/0000-0002-1703-1538"},"institutions":[{"id":"https://openalex.org/I134687103","display_name":"Nanjing Tech University","ror":"https://ror.org/03sd35x91","country_code":"CN","type":"education","lineage":["https://openalex.org/I134687103"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuanfei Dai","raw_affiliation_strings":["College of Computer and Information Sciences, Nanjing Tech University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-1703-1538","affiliations":[{"raw_affiliation_string":"College of Computer and Information Sciences, Nanjing Tech University, Nanjing, China","institution_ids":["https://openalex.org/I134687103"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.20163638,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"20","issue":"3","first_page":"1","last_page":"25"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.7055000066757202,"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":0.7055000066757202,"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":0.04820000007748604,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.03229999914765358,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/relationship-extraction","display_name":"Relationship extraction","score":0.7142000198364258},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6766999959945679},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.5788999795913696},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.550599992275238},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.5016999840736389},{"id":"https://openalex.org/keywords/generalizability-theory","display_name":"Generalizability theory","score":0.44200000166893005},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.4302000105381012},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.42730000615119934},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.4065999984741211}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8382999897003174},{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.7142000198364258},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6766999959945679},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.5788999795913696},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5679000020027161},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.550599992275238},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.5016999840736389},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4749999940395355},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.45590001344680786},{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.44200000166893005},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.4302000105381012},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.42730000615119934},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.4065999984741211},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.39320001006126404},{"id":"https://openalex.org/C28006648","wikidata":"https://www.wikidata.org/wiki/Q6934509","display_name":"Multi-task learning","level":3,"score":0.37380000948905945},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.3684999942779541},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.35109999775886536},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.336899995803833},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.32409998774528503},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.3197999894618988},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.31850001215934753},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.3052000105381012},{"id":"https://openalex.org/C197947376","wikidata":"https://www.wikidata.org/wiki/Q5155608","display_name":"Comparability","level":2,"score":0.3052000105381012},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2989000082015991},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.29760000109672546},{"id":"https://openalex.org/C72634772","wikidata":"https://www.wikidata.org/wiki/Q386824","display_name":"Data integration","level":2,"score":0.290800005197525},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.27399998903274536},{"id":"https://openalex.org/C45493050","wikidata":"https://www.wikidata.org/wiki/Q7884934","display_name":"Unified Model","level":2,"score":0.2660999894142151},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.2648000121116638},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.25929999351501465},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.2506999969482422}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3799423","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3799423","pdf_url":null,"source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Knowledge Discovery from Data","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/8","score":0.48015645146369934,"display_name":"Decent work and economic growth"}],"awards":[{"id":"https://openalex.org/G1048087334","display_name":null,"funder_award_id":"BK20220331","funder_id":"https://openalex.org/F4320322769","funder_display_name":"Natural Science Foundation of Jiangsu Province"},{"id":"https://openalex.org/G4704412119","display_name":null,"funder_award_id":"62202221","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322769","display_name":"Natural Science Foundation of Jiangsu Province","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W2081056190","https://openalex.org/W2095603291","https://openalex.org/W2107598941","https://openalex.org/W2963545917","https://openalex.org/W3034617555","https://openalex.org/W3117174129","https://openalex.org/W4322766882","https://openalex.org/W4392560653","https://openalex.org/W4392979977","https://openalex.org/W4399978597"],"related_works":[],"abstract_inverted_index":{"Joint":[0],"entity":[1,72],"and":[2,28,73,88,112,147,179,192],"relation":[3,74],"extraction":[4,117,187],"represent":[5],"a":[6,37,57],"critical":[7],"task":[8],"in":[9,47,77],"knowledge":[10],"representation,":[11],"but":[12],"often":[13],"suffers":[14],"from":[15,92],"the":[16,116,120,128,131,135,139],"bottleneck":[17],"of":[18,22,130,138],"requiring":[19],"large":[20],"amounts":[21],"labeled":[23,101,170],"data,":[24,42,102],"which":[25],"is":[26,177],"expensive":[27],"laborious":[29],"to":[30,39,69,84,114,142],"obtain.":[31],"While":[32],"Semi-Supervised":[33],"Learning":[34],"(SSL)":[35],"offers":[36],"way":[38],"leverage":[40],"unlabeled":[41,93],"traditional":[43],"methods":[44],"face":[45],"limitations":[46],"generating":[48],"high-quality,":[49],"diverse":[50,89],"augmentations":[51],"for":[52],"text.":[53],"This":[54],"article":[55],"introduces":[56],"novel":[58],"framework":[59,108,121],"that":[60,159],"synergistically":[61],"combines":[62],"SSL":[63,107,132],"with":[64,99,183],"Large":[65],"Language":[66],"Models":[67],"(LLMs)":[68],"improve":[70],"joint":[71,186],"extraction,":[75],"especially":[76],"low-resource":[78],"settings.":[79],"Our":[80],"approach":[81],"utilizes":[82],"LLMs":[83],"generate":[85],"semantically":[86],"coherent":[87],"augmented":[90,96],"data":[91,145,171],"samples.":[94],"These":[95],"samples,":[97],"along":[98],"limited":[100],"are":[103,172],"used":[104],"within":[105],"an":[106,123],"employing":[109],"consistency":[110],"regularization":[111],"pseudo-labeling":[113],"train":[115],"model.":[118],"Crucially,":[119],"incorporates":[122],"iterative":[124],"refinement":[125],"mechanism":[126],"where":[127],"performance":[129],"component":[133],"informs":[134],"parameter-efficient":[136],"fine-tuning":[137],"LLM,":[140],"leading":[141],"progressively":[143],"better":[144],"augmentation":[146],"model":[148],"accuracy.":[149],"We":[150],"demonstrate":[151],"through":[152],"extensive":[153],"experiments":[154],"on":[155],"four":[156],"benchmark":[157],"datasets":[158],"our":[160],"proposed":[161],"method":[162],"significantly":[163],"outperforms":[164],"existing":[165,185],"state-of-the-art":[166],"approaches,":[167],"particularly":[168],"when":[169],"scarce.":[173],"The":[174],"framework\u2019s":[175],"design":[176],"adaptable":[178],"can":[180],"be":[181],"integrated":[182],"various":[184],"models,":[188],"showcasing":[189],"its":[190],"generalizability":[191],"practical":[193],"utility.":[194]},"counts_by_year":[],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2026-03-01T00:00:00"}