{"id":"https://openalex.org/W2135012301","doi":"https://doi.org/10.1145/2339530.2339701","title":"Multi-domain active learning for text classification","display_name":"Multi-domain active learning for text classification","publication_year":2012,"publication_date":"2012-08-12","ids":{"openalex":"https://openalex.org/W2135012301","doi":"https://doi.org/10.1145/2339530.2339701","mag":"2135012301"},"language":"en","primary_location":{"id":"doi:10.1145/2339530.2339701","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2339530.2339701","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining","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/A5103228158","display_name":"Lianghao Li","orcid":"https://orcid.org/0000-0002-7819-7910"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Lianghao Li","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022525223","display_name":"Xiaoming Jin","orcid":"https://orcid.org/0000-0003-1531-3803"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoming Jin","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082984558","display_name":"Sinno Jialin Pan","orcid":"https://orcid.org/0000-0001-6565-3836"},"institutions":[{"id":"https://openalex.org/I3005327000","display_name":"Institute for Infocomm Research","ror":"https://ror.org/053rfa017","country_code":"SG","type":"facility","lineage":["https://openalex.org/I115228651","https://openalex.org/I3005327000","https://openalex.org/I91275662"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Sinno Jialin Pan","raw_affiliation_strings":["Institute for Infocomm Research, Singapore, Singapore"],"affiliations":[{"raw_affiliation_string":"Institute for Infocomm Research, Singapore, Singapore","institution_ids":["https://openalex.org/I3005327000"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102139311","display_name":"Jian-Tao Sun","orcid":null},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jian-Tao Sun","raw_affiliation_strings":["Microsoft Research Asia, Beijing, China","Microsoft research Asia, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Microsoft Research Asia, Beijing, China","institution_ids":["https://openalex.org/I4210113369"]},{"raw_affiliation_string":"Microsoft research Asia, Beijing, China","institution_ids":["https://openalex.org/I4210113369"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5103228158"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":6.1879,"has_fulltext":false,"cited_by_count":63,"citation_normalized_percentile":{"value":0.96334772,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1086","last_page":"1094"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","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/T12072","display_name":"Machine Learning and Algorithms","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/T10028","display_name":"Topic Modeling","score":0.9887999892234802,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9865999817848206,"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":0.7950530052185059},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7000778317451477},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6608942747116089},{"id":"https://openalex.org/keywords/active-learning","display_name":"Active learning (machine learning)","score":0.6171143054962158},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.597343921661377},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"Semi-supervised learning","score":0.525100588798523},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.5027880668640137},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4818643033504486},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.46477797627449036},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.43429797887802124},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3429561257362366}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7950530052185059},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7000778317451477},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6608942747116089},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.6171143054962158},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.597343921661377},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.525100588798523},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.5027880668640137},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4818643033504486},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.46477797627449036},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.43429797887802124},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3429561257362366},{"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/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/2339530.2339701","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2339530.2339701","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.422.799","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.422.799","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www1.i2r.a-star.edu.sg/~jspan/publications/[KDD12]Multi-Domain Active Learning for Text Classification.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W77777798","https://openalex.org/W1484084878","https://openalex.org/W1520999218","https://openalex.org/W1543338499","https://openalex.org/W1587567107","https://openalex.org/W2009207944","https://openalex.org/W2067760738","https://openalex.org/W2080021732","https://openalex.org/W2085989833","https://openalex.org/W2103077935","https://openalex.org/W2107298017","https://openalex.org/W2110518760","https://openalex.org/W2118585731","https://openalex.org/W2122838776","https://openalex.org/W2126716476","https://openalex.org/W2130903752","https://openalex.org/W2143104527","https://openalex.org/W2149684865","https://openalex.org/W2153353890","https://openalex.org/W2156104907","https://openalex.org/W2158108973","https://openalex.org/W2159574904","https://openalex.org/W2163302275","https://openalex.org/W2167595980","https://openalex.org/W2171332245","https://openalex.org/W2426031434","https://openalex.org/W2903158431","https://openalex.org/W3001645704","https://openalex.org/W4285719527","https://openalex.org/W6603082663","https://openalex.org/W6631255695","https://openalex.org/W6632666624","https://openalex.org/W6676215431"],"related_works":["https://openalex.org/W2056314584","https://openalex.org/W34092691","https://openalex.org/W2365028544","https://openalex.org/W4309984931","https://openalex.org/W4282977123","https://openalex.org/W1986633584","https://openalex.org/W2013810580","https://openalex.org/W2186210338","https://openalex.org/W2949671220","https://openalex.org/W1492505081"],"abstract_inverted_index":{"Active":[0],"learning":[1,18,92,145,238],"has":[2,20],"been":[3],"proven":[4],"to":[5,36,57,109,115,122,126,147,168],"be":[6,195,214],"effective":[7],"in":[8,133],"reducing":[9,128],"labeling":[10,131,263],"efforts":[11,132,264],"for":[12,26,40],"supervised":[13],"learning.":[14,135],"However,":[15],"existing":[16],"active":[17,91,144,237],"work":[19],"mainly":[21],"focused":[22],"on":[23,93,240,270],"training":[24],"models":[25],"a":[27,53,141,162,198,202],"single":[28],"domain.":[29],"In":[30,136,159,205],"practical":[31],"applications,":[32],"it":[33],"is":[34,124,165],"common":[35,85,111,170,179,199,218],"simultaneously":[37],"train":[38],"classifiers":[39,56],"multiple":[41,120],"domains.":[42,175],"For":[43],"example,":[44],"some":[45,84,97],"merchant":[46],"web":[47],"sites":[48],"(like":[49],"Amazon.com)":[50],"may":[51,82,104],"need":[52],"set":[54],"of":[55,62,173,220],"predict":[58],"the":[59,110,117,129,178,181,185,208,217,235,261,271],"sentiment":[60,245],"polarity":[61],"product":[63],"reviews":[64],"collected":[65],"from":[66,101,119,152],"various":[67],"domains":[68,75,103,121,154,212],"(e.g.,":[69],"electronics,":[70],"books,":[71],"shoes).":[72],"Though":[73],"different":[74,102,174],"have":[76],"their":[77],"own":[78],"unique":[79],"features,":[80],"they":[81],"share":[83],"latent":[86,171],"features.":[87,112],"If":[88],"we":[89,139],"apply":[90],"each":[94,191],"domain":[95],"separately,":[96],"data":[98,118,150,192],"instances":[99,151],"selected":[100],"contain":[105],"duplicate":[106,156,209],"knowledge":[107],"due":[108],"Therefore,":[113],"how":[114],"choose":[116],"label":[123],"crucial":[125],"further":[127],"human":[130,262],"multi-domain":[134,143],"this":[137,206],"paper,":[138],"propose":[140],"novel":[142],"framework":[146],"jointly":[148],"select":[149],"all":[153],"with":[155,234],"information":[157,210],"considered.":[158],"our":[160,232,258],"solution,":[161],"shared":[163],"subspace":[164],"first":[166],"learned":[167],"represent":[169],"features":[172,183],"By":[176],"considering":[177],"and":[180,201,224,249,268],"domain-specific":[182,203],"together,":[184],"model":[186,221],"loss":[187,222],"reduction":[188,223],"induced":[189],"by":[190,265],"instance":[193],"can":[194,213],"decomposed":[196],"into":[197,216,226],"part":[200,219],"part.":[204],"way,":[207],"across":[211],"encoded":[215],"taken":[225],"account":[227],"when":[228],"querying.":[229],"We":[230],"compare":[231],"method":[233,259],"state-of-the-art":[236],"approaches":[239],"several":[241],"text":[242],"classification":[243,248],"tasks:":[244],"classification,":[246],"newsgroup":[247],"email":[250],"spam":[251],"filtering.":[252],"The":[253],"experiment":[254],"results":[255],"show":[256],"that":[257],"reduces":[260],"33.2%,":[266],"42.9%":[267],"68.7%":[269],"three":[272],"tasks,":[273],"respectively.":[274]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":8},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":11},{"year":2016,"cited_by_count":9},{"year":2015,"cited_by_count":5},{"year":2014,"cited_by_count":6},{"year":2013,"cited_by_count":2},{"year":2012,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}