{"id":"https://openalex.org/W2011627645","doi":"https://doi.org/10.1145/2600428.2609470","title":"Co-training on authorship attribution with very fewlabeled examples","display_name":"Co-training on authorship attribution with very fewlabeled examples","publication_year":2014,"publication_date":"2014-07-03","ids":{"openalex":"https://openalex.org/W2011627645","doi":"https://doi.org/10.1145/2600428.2609470","mag":"2011627645"},"language":"en","primary_location":{"id":"doi:10.1145/2600428.2609470","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2600428.2609470","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval","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/A5040759280","display_name":"Tieyun Qian","orcid":"https://orcid.org/0000-0003-4667-5794"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Tieyun Qian","raw_affiliation_strings":["Wuhan University, Wuhan, China","Wuhan University, Wuhan China#TAB#"],"affiliations":[{"raw_affiliation_string":"Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]},{"raw_affiliation_string":"Wuhan University, Wuhan China#TAB#","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100339927","display_name":"Bing Liu","orcid":"https://orcid.org/0000-0002-4096-6980"},"institutions":[{"id":"https://openalex.org/I39422238","display_name":"University of Illinois Chicago","ror":"https://ror.org/02mpq6x41","country_code":"US","type":"education","lineage":["https://openalex.org/I39422238"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bing Liu","raw_affiliation_strings":["University of Illinois at Chicago, Chicago, USA"],"affiliations":[{"raw_affiliation_string":"University of Illinois at Chicago, Chicago, USA","institution_ids":["https://openalex.org/I39422238"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033128202","display_name":"Ming Zhong","orcid":"https://orcid.org/0000-0001-9376-818X"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ming Zhong","raw_affiliation_strings":["Wuhan University, Wuhan, China","Wuhan University, Wuhan China#TAB#"],"affiliations":[{"raw_affiliation_string":"Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]},{"raw_affiliation_string":"Wuhan University, Wuhan China#TAB#","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060931310","display_name":"Guoliang He","orcid":"https://orcid.org/0000-0002-9637-2006"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guoliang He","raw_affiliation_strings":["Wuhan University, Wuhan, China","Wuhan University, Wuhan China#TAB#"],"affiliations":[{"raw_affiliation_string":"Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]},{"raw_affiliation_string":"Wuhan University, Wuhan China#TAB#","institution_ids":["https://openalex.org/I37461747"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5040759280"],"corresponding_institution_ids":["https://openalex.org/I37461747"],"apc_list":null,"apc_paid":null,"fwci":2.045,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.88962313,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"903","last_page":"906"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12380","display_name":"Authorship Attribution and Profiling","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/T12380","display_name":"Authorship Attribution and Profiling","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/T10028","display_name":"Topic Modeling","score":0.996999979019165,"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/T12262","display_name":"Hate Speech and Cyberbullying Detection","score":0.9922000169754028,"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/computer-science","display_name":"Computer science","score":0.8126555681228638},{"id":"https://openalex.org/keywords/authorship-attribution","display_name":"Authorship attribution","score":0.7864745855331421},{"id":"https://openalex.org/keywords/co-training","display_name":"Co-training","score":0.7609838247299194},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.7248242497444153},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6598060131072998},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6284359097480774},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5960081815719604},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.55905681848526},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.48239079117774963},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4814288318157196},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.4731755554676056},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.32759523391723633},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"Semi-supervised learning","score":0.0980934202671051}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8126555681228638},{"id":"https://openalex.org/C3020202489","wikidata":"https://www.wikidata.org/wiki/Q2032038","display_name":"Authorship attribution","level":2,"score":0.7864745855331421},{"id":"https://openalex.org/C2776959682","wikidata":"https://www.wikidata.org/wiki/Q17005296","display_name":"Co-training","level":3,"score":0.7609838247299194},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.7248242497444153},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6598060131072998},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6284359097480774},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5960081815719604},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.55905681848526},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.48239079117774963},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4814288318157196},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4731755554676056},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.32759523391723633},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.0980934202671051},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2600428.2609470","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2600428.2609470","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W199088597","https://openalex.org/W277867124","https://openalex.org/W1524973551","https://openalex.org/W1541095199","https://openalex.org/W1980150549","https://openalex.org/W1992259767","https://openalex.org/W2014864037","https://openalex.org/W2025882237","https://openalex.org/W2047756776","https://openalex.org/W2048679005","https://openalex.org/W2072828027","https://openalex.org/W2097606805","https://openalex.org/W2103167816","https://openalex.org/W2116876181","https://openalex.org/W2118585731","https://openalex.org/W2126631960","https://openalex.org/W2159642183","https://openalex.org/W2407404459","https://openalex.org/W3001645704"],"related_works":["https://openalex.org/W2251208230","https://openalex.org/W2098708659","https://openalex.org/W264117909","https://openalex.org/W2215421138","https://openalex.org/W2981877337","https://openalex.org/W277867124","https://openalex.org/W3203938600","https://openalex.org/W2169074127","https://openalex.org/W2117921985","https://openalex.org/W2165396616"],"abstract_inverted_index":{"Authorship":[0],"attribution":[1,147],"(AA)":[2],"aims":[3],"to":[4,42,69,88,98,107,123],"identify":[5,90],"the":[6,34,53,72,91,100,125,141,189,193,205,228,232],"authors":[7,92],"of":[8,11,26,47,93,128,143],"a":[9,23,44,62,83,94,118,201,236],"set":[10,25,46],"documents.":[12,130],"Traditional":[13],"studies":[14],"in":[15,33,52,186],"this":[16,79],"area":[17],"often":[18],"assume":[19],"that":[20,178,234],"there":[21],"are":[22,66,157],"large":[24,45,126],"labeled":[27,48,150],"documents":[28],"available":[29],"for":[30,75,145,183],"training.":[31],"However,":[32],"real":[35],"life,":[36],"it":[37,226],"is":[38,106,121,180,227],"hard":[39],"or":[40],"expensive":[41],"collect":[43],"data.":[49,151],"For":[50],"example,":[51],"online":[54],"review":[55],"domain,":[56],"most":[57],"reviewers":[58],"(authors)":[59],"only":[60],"write":[61],"few":[63,95],"reviews,":[64],"which":[65],"not":[67,209],"enough":[68],"serve":[70],"as":[71,112],"training":[73,101,134],"data":[74,97],"accurate":[76],"classification.":[77],"In":[78],"paper,":[80],"we":[81,138],"present":[82],"novel":[84],"two-view":[85],"co-training":[86,119,144,185,206],"framework":[87,207],"iteratively":[89],"unlabeled":[96,129],"augment":[99],"set.":[102],"The":[103,174],"key":[104],"idea":[105],"first":[108],"represent":[109],"each":[110],"document":[111],"several":[113],"distinct":[114],"views,":[115],"and":[116,154,162,168,171,188,219,231],"then":[117],"technique":[120],"adopted":[122],"exploit":[124],"amount":[127],"Starting":[131],"from":[132,216,222],"10":[133],"texts":[135],"per":[136],"author,":[137],"systematically":[139],"evaluate":[140],"effectiveness":[142],"authorship":[146],"with":[148,200],"limited":[149],"Two":[152],"methods":[153],"three":[155,196],"views":[156,197,218],"investigated:":[158],"logistic":[159],"regression":[160],"(LR)":[161],"support":[163],"vector":[164],"machines":[165],"(SVM)":[166],"methods,":[167],"character,":[169],"lexical,":[170],"syntactic":[172],"views.":[173],"experimental":[175],"results":[176],"show":[177],"LR":[179,202],"particularly":[181],"effective":[182],"improving":[184],"AA,":[187],"lexical":[190],"view":[191,233],"performs":[192],"best":[194],"among":[195],"when":[198],"combined":[199],"classifier.":[203],"Furthermore,":[204],"does":[208],"make":[210],"much":[211],"difference":[212],"between":[213],"one":[214,223],"classifier":[215],"two":[217,220],"classifiers":[221],"view.":[224],"Instead,":[225],"learning":[229],"approach":[230],"plays":[235],"critical":[237],"role.":[238]},"counts_by_year":[{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":3},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}