{"id":"https://openalex.org/W2075178605","doi":"https://doi.org/10.5220/0005069400310042","title":"Cross-domain Text Classification through Iterative Refining of Target Categories Representations","display_name":"Cross-domain Text Classification through Iterative Refining of Target Categories Representations","publication_year":2014,"publication_date":"2014-01-01","ids":{"openalex":"https://openalex.org/W2075178605","doi":"https://doi.org/10.5220/0005069400310042","mag":"2075178605"},"language":"en","primary_location":{"id":"doi:10.5220/0005069400310042","is_oa":true,"landing_page_url":"https://doi.org/10.5220/0005069400310042","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Conference on Knowledge Discovery and Information Retrieval","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.5220/0005069400310042","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5084939540","display_name":"Giacomo Domeniconi","orcid":"https://orcid.org/0000-0003-2797-1547"},"institutions":[{"id":"https://openalex.org/I9360294","display_name":"University of Bologna","ror":"https://ror.org/01111rn36","country_code":"IT","type":"education","lineage":["https://openalex.org/I9360294"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Giacomo Domeniconi","raw_affiliation_strings":["Universit\u00e0 degli Studi di Bologna, Italy"],"affiliations":[{"raw_affiliation_string":"Universit\u00e0 degli Studi di Bologna, Italy","institution_ids":["https://openalex.org/I9360294"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079648393","display_name":"Gianluca Moro","orcid":"https://orcid.org/0000-0002-3663-7877"},"institutions":[{"id":"https://openalex.org/I9360294","display_name":"University of Bologna","ror":"https://ror.org/01111rn36","country_code":"IT","type":"education","lineage":["https://openalex.org/I9360294"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Gianluca Moro","raw_affiliation_strings":["Universit\u00e0 degli Studi di Bologna, Italy"],"affiliations":[{"raw_affiliation_string":"Universit\u00e0 degli Studi di Bologna, Italy","institution_ids":["https://openalex.org/I9360294"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069989399","display_name":"Roberto Pasolini","orcid":null},"institutions":[{"id":"https://openalex.org/I9360294","display_name":"University of Bologna","ror":"https://ror.org/01111rn36","country_code":"IT","type":"education","lineage":["https://openalex.org/I9360294"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Roberto Pasolini","raw_affiliation_strings":["Universit\u00e0 degli Studi di Bologna, Italy"],"affiliations":[{"raw_affiliation_string":"Universit\u00e0 degli Studi di Bologna, Italy","institution_ids":["https://openalex.org/I9360294"]}]},{"author_position":"last","author":{"id":null,"display_name":"Claudio Sartori","orcid":null},"institutions":[{"id":"https://openalex.org/I9360294","display_name":"University of Bologna","ror":"https://ror.org/01111rn36","country_code":"IT","type":"education","lineage":["https://openalex.org/I9360294"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Claudio Sartori","raw_affiliation_strings":["Universit\u00e0 degli Studi di Bologna, Italy"],"affiliations":[{"raw_affiliation_string":"Universit\u00e0 degli Studi di Bologna, Italy","institution_ids":["https://openalex.org/I9360294"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5084939540"],"corresponding_institution_ids":["https://openalex.org/I9360294"],"apc_list":null,"apc_paid":null,"fwci":2.96,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":{"value":0.92294311,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"31","last_page":"42"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","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/T11550","display_name":"Text and Document Classification Technologies","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/T11644","display_name":"Spam and Phishing Detection","score":0.9898999929428101,"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"}},{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.984000027179718,"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":0.7779985666275024},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.716726541519165},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.7132644653320312},{"id":"https://openalex.org/keywords/cosine-similarity","display_name":"Cosine similarity","score":0.6606713533401489},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.633879542350769},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6179724931716919},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5601673722267151},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.535599946975708},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.43363243341445923},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.41180071234703064},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4094848334789276},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3817152976989746},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3663453161716461},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.32005998492240906},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.17434164881706238}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7779985666275024},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.716726541519165},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.7132644653320312},{"id":"https://openalex.org/C2780762811","wikidata":"https://www.wikidata.org/wiki/Q1784941","display_name":"Cosine similarity","level":3,"score":0.6606713533401489},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.633879542350769},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6179724931716919},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5601673722267151},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.535599946975708},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.43363243341445923},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.41180071234703064},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4094848334789276},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3817152976989746},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3663453161716461},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32005998492240906},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17434164881706238},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","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:10.5220/0005069400310042","is_oa":true,"landing_page_url":"https://doi.org/10.5220/0005069400310042","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Conference on Knowledge Discovery and Information Retrieval","raw_type":"proceedings-article"},{"id":"pmh:oai:cris.unibo.it:11585/417780","is_oa":false,"landing_page_url":"http://hdl.handle.net/11585/417780","pdf_url":null,"source":{"id":"https://openalex.org/S4306402579","display_name":"Archivio istituzionale della ricerca (Alma Mater Studiorum Universit\u00e0 di Bologna)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210117483","host_organization_name":"Istituto di Ematologia di Bologna","host_organization_lineage":["https://openalex.org/I4210117483"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":{"id":"doi:10.5220/0005069400310042","is_oa":true,"landing_page_url":"https://doi.org/10.5220/0005069400310042","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Conference on Knowledge Discovery and Information Retrieval","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W665341","https://openalex.org/W1487445520","https://openalex.org/W1568869800","https://openalex.org/W1880262756","https://openalex.org/W1973948212","https://openalex.org/W1978394996","https://openalex.org/W1994966918","https://openalex.org/W1998894210","https://openalex.org/W2000987725","https://openalex.org/W2005422315","https://openalex.org/W2005758492","https://openalex.org/W2032536435","https://openalex.org/W2034368206","https://openalex.org/W2062179223","https://openalex.org/W2068463433","https://openalex.org/W2095579012","https://openalex.org/W2096152098","https://openalex.org/W2098162425","https://openalex.org/W2103851188","https://openalex.org/W2107298017","https://openalex.org/W2111730140","https://openalex.org/W2112483442","https://openalex.org/W2115191221","https://openalex.org/W2115403315","https://openalex.org/W2118020653","https://openalex.org/W2120262857","https://openalex.org/W2120354757","https://openalex.org/W2120779048","https://openalex.org/W2133990480","https://openalex.org/W2148820067","https://openalex.org/W2149684865","https://openalex.org/W2158108973","https://openalex.org/W2162161511","https://openalex.org/W2165698076","https://openalex.org/W2170654002","https://openalex.org/W2171068337","https://openalex.org/W2181470043"],"related_works":["https://openalex.org/W2389818373","https://openalex.org/W2220831889","https://openalex.org/W4312683641","https://openalex.org/W3027421045","https://openalex.org/W2576320324","https://openalex.org/W2980386803","https://openalex.org/W3215994059","https://openalex.org/W2319823519","https://openalex.org/W4206798987","https://openalex.org/W2801158176"],"abstract_inverted_index":{"Cross-domain":[0],"text":[1,174],"classification":[2],"deals":[3],"with":[4,25,134,189],"predicting":[5],"topic":[6,101],"labels":[7],"for":[8,59,83,107,159,193],"documents":[9,20,40,60,168],"in":[10,21,161],"a":[11,22,55,75,92,139],"target":[12,50,132,163],"domain":[13,44],"by":[14,38,53,127,138],"leverag-":[15],"ing":[16],"knowledge":[17],"from":[18,41,119],"pre-labeled":[19],"source":[23,43,121],"domain,":[24,164],"different":[26,29],"terms":[27],"or":[28,52,183],"distributions":[30],"thereof.":[31],"Methods":[32],"exist":[33],"to":[34,45,48,74,86,109,155,166,185],"address":[35],"this":[36,178],"problem":[37],"re-weighting":[39],"the":[42,49,68,110,129,150,162],"transfer":[46],"them":[47],"one":[51],"finding":[54],"common":[56,172],"feature":[57],"space":[58],"of":[61,70,77,100,112],"both":[62],"domains;":[63],"they":[64],"often":[65],"re-":[66],"quire":[67],"combination":[69],"complex":[71],"techniques,":[72],"leading":[73],"number":[76],"parameters":[78],"which":[79,103],"must":[80],"be":[81,105],"tuned":[82],"each":[84],"dataset":[85],"yield":[87],"optimal":[88],"performances.":[89],"We":[90],"present":[91],"simpler":[93],"method":[94],"based":[95,143],"on":[96,144,171],"creating":[97],"explicit":[98],"representations":[99,115,158],"categories,":[102],"can":[104],"compared":[106],"similarity":[108],"ones":[111],"documents.":[113],"Categories":[114],"are":[116,124],"initially":[117],"built":[118,147],"relevant":[120],"documents,":[122,133],"then":[123],"iteratively":[125],"refined":[126],"considering":[128],"most":[130],"similar":[131],"relatedness":[135],"being":[136],"measured":[137],"simple":[140],"regression":[141],"model":[142],"cosine":[145],"similarity,":[146],"once":[148],"at":[149],"begin.":[151],"This":[152],"expectedly":[153],"leads":[154],"obtain":[156],"accurate":[157],"categories":[160],"used":[165],"classify":[167],"therein.":[169],"Experiments":[170],"benchmark":[173],"collections":[175],"show":[176],"that":[177],"approach":[179],"obtains":[180],"results":[181],"better":[182],"comparable":[184],"other":[186],"methods,":[187],"obtained":[188],"fixed":[190],"empirical":[191],"values":[192],"its":[194],"few":[195],"parameters.":[196]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":3}],"updated_date":"2026-04-16T08:26:57.006410","created_date":"2025-10-10T00:00:00"}
