{"id":"https://openalex.org/W3023037848","doi":"https://doi.org/10.1145/3340531.3411858","title":"MICK: A Meta-Learning Framework for Few-shot Relation Classification with Small Training Data","display_name":"MICK: A Meta-Learning Framework for Few-shot Relation Classification with Small Training Data","publication_year":2020,"publication_date":"2020-10-19","ids":{"openalex":"https://openalex.org/W3023037848","doi":"https://doi.org/10.1145/3340531.3411858","mag":"3023037848"},"language":"en","primary_location":{"id":"doi:10.1145/3340531.3411858","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3340531.3411858","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM International Conference on Information &amp; Knowledge Management","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2004.14164","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Xiaoqing Geng","orcid":null},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoqing Geng","raw_affiliation_strings":["Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xiwen Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiwen Chen","raw_affiliation_strings":["University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Kenny Q. Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kenny Q. Zhu","raw_affiliation_strings":["Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Libin Shen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Libin Shen","raw_affiliation_strings":["Leyan Tech, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Leyan Tech, Shanghai, China","institution_ids":[]}]},{"author_position":"last","author":{"id":null,"display_name":"Yinggong Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yinggong Zhao","raw_affiliation_strings":["Leyan Tech, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Leyan Tech, Shanghai, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"415","last_page":"424"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9991000294685364,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9991000294685364,"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/T10028","display_name":"Topic Modeling","score":0.989799976348877,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9894999861717224,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/relation","display_name":"Relation (database)","score":0.7872999906539917},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6200000047683716},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.5332000255584717},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5090000033378601},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5045999884605408},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.4819999933242798},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.4738999903202057},{"id":"https://openalex.org/keywords/domain-knowledge","display_name":"Domain knowledge","score":0.4242999851703644}],"concepts":[{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.7872999906539917},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7228000164031982},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6200000047683716},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5666000247001648},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.5332000255584717},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5221999883651733},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5090000033378601},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5045999884605408},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.4819999933242798},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.4738999903202057},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.4242999851703644},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4203000068664551},{"id":"https://openalex.org/C2776959682","wikidata":"https://www.wikidata.org/wiki/Q17005296","display_name":"Co-training","level":3,"score":0.37599998712539673},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.35589998960494995},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.35100001096725464},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.29589998722076416},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.2782999873161316},{"id":"https://openalex.org/C86037889","wikidata":"https://www.wikidata.org/wiki/Q4330127","display_name":"Learning to rank","level":3,"score":0.2777000069618225},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2768999934196472},{"id":"https://openalex.org/C2778334786","wikidata":"https://www.wikidata.org/wiki/Q1586270","display_name":"Variation (astronomy)","level":2,"score":0.263700008392334}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3340531.3411858","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3340531.3411858","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM International Conference on Information &amp; Knowledge Management","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2004.14164","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2004.14164","pdf_url":"https://arxiv.org/pdf/2004.14164","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":"pmh:oai:arXiv.org:2004.14164","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2004.14164","pdf_url":"https://arxiv.org/pdf/2004.14164","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"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1604644367","https://openalex.org/W2094728533","https://openalex.org/W2099779943","https://openalex.org/W2251135946","https://openalex.org/W2604763608","https://openalex.org/W2759996146","https://openalex.org/W2889234142","https://openalex.org/W2909383239","https://openalex.org/W2951615109","https://openalex.org/W2962785888","https://openalex.org/W2963777632","https://openalex.org/W2964217331","https://openalex.org/W2971136144"],"related_works":[],"abstract_inverted_index":{"Few-shot":[0],"relation":[1,55,121,134],"classification":[2,122,148],"seeks":[3],"to":[4,75,88],"classify":[5,76],"incoming":[6],"query":[7,77],"instances":[8,87],"after":[9],"meeting":[10],"only":[11,73],"few":[12],"support":[13,86],"instances.":[14],"This":[15],"ability":[16],"is":[17,58,65],"gained":[18],"by":[19,37,105],"training":[20,46,63,130,156,165],"with":[21,127,162],"large":[22,164],"amount":[23,41],"of":[24,42],"in-domain":[25],"annotated":[26],"data.":[27,166],"In":[28,68],"this":[29,69],"paper,":[30],"we":[31,110],"tackle":[32],"an":[33],"even":[34],"harder":[35],"problem":[36],"further":[38],"limiting":[39],"the":[40,62,85,116,151],"data":[43,64,131],"available":[44],"at":[45],"time.":[47],"We":[48],"propose":[49],"a":[50,97,112,119],"few-shot":[51,120],"learning":[52],"framework":[53,94,141],"for":[54,99,145],"classification,":[56],"which":[57],"particularly":[59],"powerful":[60],"when":[61],"very":[66],"small.":[67],"framework,":[70],"models":[71,104],"not":[72],"strive":[74],"instances,":[78],"but":[79],"also":[80,95],"seek":[81],"underlying":[82,147],"knowledge":[83,102],"about":[84],"obtain":[89],"better":[90],"instance":[91],"representations.":[92],"The":[93],"includes":[96],"method":[98],"aggregating":[100],"cross-domain":[101],"into":[103],"open-source":[106],"task":[107],"enrichment.":[108],"Additionally,":[109],"construct":[111],"brand":[113],"new":[114],"dataset:":[115],"TinyRel-CM":[117],"dataset,":[118],"dataset":[123],"in":[124],"health":[125],"domain":[126],"purposely":[128],"small":[129,155],"and":[132,158],"challenging":[133],"classes.":[135],"Experimental":[136],"results":[137,153,161],"demonstrate":[138],"that":[139],"our":[140],"brings":[142],"performance":[143],"gains":[144],"most":[146],"models,":[149],"outperforms":[150],"state-of-the-art":[152],"given":[154],"data,":[157],"achieves":[159],"competitive":[160],"sufficiently":[163]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2020-05-13T00:00:00"}
