{"id":"https://openalex.org/W4392567319","doi":"https://doi.org/10.1109/access.2024.3374727","title":"Fighting Against the Repetitive Training and Sample Dependency Problem in Few-Shot Named Entity Recognition","display_name":"Fighting Against the Repetitive Training and Sample Dependency Problem in Few-Shot Named Entity Recognition","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4392567319","doi":"https://doi.org/10.1109/access.2024.3374727"},"language":"en","primary_location":{"id":"doi:10.1109/access.2024.3374727","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3374727","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10463035.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10463035.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5066049616","display_name":"Chang Tian","orcid":"https://orcid.org/0000-0001-9271-389X"},"institutions":[{"id":"https://openalex.org/I99464096","display_name":"KU Leuven","ror":"https://ror.org/05f950310","country_code":"BE","type":"education","lineage":["https://openalex.org/I99464096"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Chang Tian","raw_affiliation_strings":["Department of Computer Science, LIIR, KU Leuven, Leuven, Belgium","LIIR, Department of Computer Science, KU Leuven, Leuven, Belgium"],"raw_orcid":"https://orcid.org/0000-0001-9271-389X","affiliations":[{"raw_affiliation_string":"Department of Computer Science, LIIR, KU Leuven, Leuven, Belgium","institution_ids":["https://openalex.org/I99464096"]},{"raw_affiliation_string":"LIIR, Department of Computer Science, KU Leuven, Leuven, Belgium","institution_ids":["https://openalex.org/I99464096"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038386528","display_name":"Wenpeng Yin","orcid":"https://orcid.org/0000-0002-3154-1139"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wenpeng Yin","raw_affiliation_strings":["AI4Research Laboratory, The Pennsylvania State University, State College, PA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"AI4Research Laboratory, The Pennsylvania State University, State College, PA, USA","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039044593","display_name":"Dan Li","orcid":"https://orcid.org/0000-0001-6381-1087"},"institutions":[{"id":"https://openalex.org/I1318003438","display_name":"RELX Group (Netherlands)","ror":"https://ror.org/02scfj030","country_code":"NL","type":"company","lineage":["https://openalex.org/I1318003438","https://openalex.org/I4210160603"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Dan Li","raw_affiliation_strings":["Elsevier, Amsterdam, The Netherlands","Elsevier, Amsterdam, NX, Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Elsevier, Amsterdam, The Netherlands","institution_ids":["https://openalex.org/I1318003438"]},{"raw_affiliation_string":"Elsevier, Amsterdam, NX, Netherlands","institution_ids":["https://openalex.org/I1318003438"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075796989","display_name":"Marie\u2010Francine Moens","orcid":"https://orcid.org/0000-0002-3732-9323"},"institutions":[{"id":"https://openalex.org/I99464096","display_name":"KU Leuven","ror":"https://ror.org/05f950310","country_code":"BE","type":"education","lineage":["https://openalex.org/I99464096"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Marie-Francine Moens","raw_affiliation_strings":["Department of Computer Science, LIIR, KU Leuven, Leuven, Belgium","LIIR, Department of Computer Science, KU Leuven, Leuven, Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, LIIR, KU Leuven, Leuven, Belgium","institution_ids":["https://openalex.org/I99464096"]},{"raw_affiliation_string":"LIIR, Department of Computer Science, KU Leuven, Leuven, Belgium","institution_ids":["https://openalex.org/I99464096"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.6671,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":{"value":0.85344226,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":"12","issue":null,"first_page":"37600","last_page":"37614"},"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":0.9987999796867371,"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/T10260","display_name":"Software Engineering Research","score":0.9359999895095825,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/computer-science","display_name":"Computer science","score":0.8446543216705322},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.6526944637298584},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6036226749420166},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5564842820167542},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5154638886451721},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5124281048774719},{"id":"https://openalex.org/keywords/dependency","display_name":"Dependency (UML)","score":0.49199602007865906},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.48946869373321533},{"id":"https://openalex.org/keywords/named-entity-recognition","display_name":"Named-entity recognition","score":0.4456958770751953},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.4176156520843506},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4174922704696655},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3555050194263458},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.33793073892593384},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.32504355907440186},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3221029043197632}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8446543216705322},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.6526944637298584},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6036226749420166},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5564842820167542},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5154638886451721},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5124281048774719},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.49199602007865906},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.48946869373321533},{"id":"https://openalex.org/C2779135771","wikidata":"https://www.wikidata.org/wiki/Q403574","display_name":"Named-entity recognition","level":3,"score":0.4456958770751953},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4176156520843506},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4174922704696655},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3555050194263458},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.33793073892593384},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32504355907440186},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3221029043197632},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"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/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/access.2024.3374727","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3374727","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10463035.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:lirias2repo.kuleuven.be:20.500.12942/740421","is_oa":true,"landing_page_url":"https://lirias.kuleuven.be/handle/20.500.12942/740421","pdf_url":"https://lirias.kuleuven.be/retrieve/b9f021af-8e6d-4abe-b681-fbf06f9d8de5","source":{"id":"https://openalex.org/S7407055369","display_name":"Lirias","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Ieee Access, vol. 12, (37600-37614)","raw_type":"info:eu-repo/semantics/article"},{"id":"pmh:oai:doaj.org/article:a6431c71881c4d1f8e1029b39c1fda39","is_oa":true,"landing_page_url":"https://doaj.org/article/a6431c71881c4d1f8e1029b39c1fda39","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 12, Pp 37600-37614 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2024.3374727","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3374727","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10463035.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1821211423","display_name":null,"funder_award_id":"H2020-ERC-2017-ADG 788506","funder_id":"https://openalex.org/F4320334678","funder_display_name":"European Research Council"},{"id":"https://openalex.org/G338431185","display_name":null,"funder_award_id":"ERC-2017-ADG","funder_id":"https://openalex.org/F4320332999","funder_display_name":"Horizon 2020 Framework Programme"},{"id":"https://openalex.org/G5591719479","display_name":null,"funder_award_id":"788506","funder_id":"https://openalex.org/F4320332999","funder_display_name":"Horizon 2020 Framework Programme"}],"funders":[{"id":"https://openalex.org/F4320322725","display_name":"China Scholarship Council","ror":"https://ror.org/04atp4p48"},{"id":"https://openalex.org/F4320332999","display_name":"Horizon 2020 Framework Programme","ror":"https://ror.org/00k4n6c32"},{"id":"https://openalex.org/F4320334678","display_name":"European Research Council","ror":"https://ror.org/0472cxd90"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4392567319.pdf","grobid_xml":"https://content.openalex.org/works/W4392567319.grobid-xml"},"referenced_works_count":51,"referenced_works":["https://openalex.org/W1557642470","https://openalex.org/W2144578941","https://openalex.org/W2159329043","https://openalex.org/W2299976354","https://openalex.org/W2626967794","https://openalex.org/W2757689523","https://openalex.org/W2760505947","https://openalex.org/W2808874433","https://openalex.org/W2896457183","https://openalex.org/W2904415434","https://openalex.org/W2908510526","https://openalex.org/W2963563735","https://openalex.org/W2997124358","https://openalex.org/W3011594683","https://openalex.org/W3035044482","https://openalex.org/W3046698617","https://openalex.org/W3099591453","https://openalex.org/W3104774463","https://openalex.org/W3105470358","https://openalex.org/W3131198033","https://openalex.org/W3176489198","https://openalex.org/W3177312484","https://openalex.org/W3186405834","https://openalex.org/W3195893957","https://openalex.org/W3196336822","https://openalex.org/W3199662997","https://openalex.org/W3200787399","https://openalex.org/W3201983976","https://openalex.org/W3207474960","https://openalex.org/W3212290569","https://openalex.org/W3212769596","https://openalex.org/W4205312732","https://openalex.org/W4224095956","https://openalex.org/W4287890932","https://openalex.org/W4365512576","https://openalex.org/W4385570099","https://openalex.org/W4385570976","https://openalex.org/W4385572195","https://openalex.org/W4386609270","https://openalex.org/W4387068131","https://openalex.org/W4387998178","https://openalex.org/W4389518918","https://openalex.org/W4389519297","https://openalex.org/W4389519834","https://openalex.org/W4389520152","https://openalex.org/W4389523704","https://openalex.org/W6717697761","https://openalex.org/W6736057607","https://openalex.org/W6760378562","https://openalex.org/W6778883912","https://openalex.org/W6843893154"],"related_works":["https://openalex.org/W3199871245","https://openalex.org/W2915436880","https://openalex.org/W1964783010","https://openalex.org/W3198729192","https://openalex.org/W3121292129","https://openalex.org/W2067317451","https://openalex.org/W4295038225","https://openalex.org/W4285819037","https://openalex.org/W3200093942","https://openalex.org/W2955175590"],"abstract_inverted_index":{"Few-shot":[0],"named":[1],"entity":[2,24,108],"recognition":[3],"(NER)":[4],"systems":[5],"recognize":[6],"entities":[7],"using":[8],"a":[9,19,89,147,179],"few":[10],"labeled":[11],"training":[12,54,70,172,204],"examples.":[13],"The":[14],"general":[15],"pipeline":[16,165],"consists":[17],"of":[18,71,173,194],"span":[20,38,50,57,74,77,149,166],"detector":[21,51,150,167],"to":[22,32,35,45,62,68,110,162,168,184],"identify":[23],"spans":[25],"in":[26,221],"text":[27],"and":[28,100,206,242],"an":[29,139],"entity-type":[30,81,101,131,187],"classifier":[31],"assign":[33],"types":[34],"entities.":[36],"Current":[37],"detectors":[39],"rely":[40],"on":[41,55,154,191,217],"extensive":[42,215],"manual":[43],"labeling":[44],"guide":[46],"training.":[47],"Almost":[48],"every":[49],"requires":[52],"initial":[53],"basic":[56,73,174],"features":[58,75],"followed":[59],"by":[60],"adaptation":[61],"task-specific":[63],"features.":[64,175],"This":[65],"process":[66],"leads":[67],"repetitive":[69,171],"the":[72,94,97,105,111,118,125,164,170,237],"among":[76],"detectors.":[78],"Additionally,":[79],"metric-based":[80],"classifiers,":[82],"such":[83],"as":[84,212],"prototypical":[85],"networks,":[86],"typically":[87],"employ":[88],"specific":[90],"metric":[91],"that":[92,151],"gauges":[93],"distance":[95],"between":[96],"query":[98,112],"sample":[99,119],"referents,":[102,188],"ultimately":[103],"assigning":[104],"most":[106],"probable":[107],"type":[109],"sample.":[113],"However,":[114],"these":[115,135],"classifiers":[116],"encounter":[117],"dependency":[120],"problem,":[121],"primarily":[122],"stemming":[123],"from":[124],"limited":[126],"samples":[127,193],"available":[128],"for":[129],"each":[130,195],"referent.":[132],"To":[133],"address":[134],"challenges,":[136],"we":[137,145,177],"proposed":[138],"improved":[140],"few-shot":[141,192,223],"NER":[142,224],"pipeline.":[143],"First,":[144],"introduce":[146],"steppingstone":[148],"is":[152],"pre-trained":[153],"open-domain":[155],"Wikipedia":[156],"data.":[157],"It":[158],"can":[159],"be":[160],"used":[161],"initialize":[163],"reduce":[169],"Second,":[176],"leverage":[178],"large":[180],"language":[181],"model":[182,198,227,243],"(LLM)":[183],"set":[185],"reliable":[186],"eliminating":[189],"reliance":[190],"type.":[196],"Our":[197],"exhibits":[199],"superior":[200],"performance":[201],"with":[202,210],"fewer":[203],"steps":[205],"human-labeled":[207],"data":[208],"compared":[209],"baselines,":[211,230],"demonstrated":[213],"through":[214],"experiments":[216],"various":[218],"datasets.":[219],"Particularly":[220],"fine-grained":[222],"settings,":[225],"our":[226],"outperforms":[228],"strong":[229],"including":[231],"ChatGPT.":[232],"We":[233],"will":[234],"publicly":[235],"release":[236],"code,":[238],"datasets,":[239],"LLM":[240],"outputs,":[241],"checkpoints.":[244]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":4}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}