{"id":"https://openalex.org/W2250924865","doi":"https://doi.org/10.18653/v1/d15-1283","title":"Co-Training for Topic Classification of Scholarly Data","display_name":"Co-Training for Topic Classification of Scholarly Data","publication_year":2015,"publication_date":"2015-01-01","ids":{"openalex":"https://openalex.org/W2250924865","doi":"https://doi.org/10.18653/v1/d15-1283","mag":"2250924865"},"language":"en","primary_location":{"id":"doi:10.18653/v1/d15-1283","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d15-1283","pdf_url":"https://www.aclweb.org/anthology/D15-1283.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/D15-1283.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5089085275","display_name":"Cornelia Caragea","orcid":"https://orcid.org/0000-0002-5664-2163"},"institutions":[{"id":"https://openalex.org/I123534392","display_name":"University of North Texas","ror":"https://ror.org/00v97ad02","country_code":"US","type":"education","lineage":["https://openalex.org/I123534392"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Cornelia Caragea","raw_affiliation_strings":["Computer Science and Engineering, University of North Texas, TX, USA"],"affiliations":[{"raw_affiliation_string":"Computer Science and Engineering, University of North Texas, TX, USA","institution_ids":["https://openalex.org/I123534392"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031354630","display_name":"Florin Bulgarov","orcid":null},"institutions":[{"id":"https://openalex.org/I123534392","display_name":"University of North Texas","ror":"https://ror.org/00v97ad02","country_code":"US","type":"education","lineage":["https://openalex.org/I123534392"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Florin Bulgarov","raw_affiliation_strings":["Computer Science and Engineering, University of North Texas, TX, USA"],"affiliations":[{"raw_affiliation_string":"Computer Science and Engineering, University of North Texas, TX, USA","institution_ids":["https://openalex.org/I123534392"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082450455","display_name":"Rada Mihalcea","orcid":"https://orcid.org/0000-0002-0767-6703"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan\u2013Ann Arbor","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rada Mihalcea","raw_affiliation_strings":["Computer Science and Engineering, University of Michigan, MI, USA"],"affiliations":[{"raw_affiliation_string":"Computer Science and Engineering, University of Michigan, MI, USA","institution_ids":["https://openalex.org/I27837315"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5089085275"],"corresponding_institution_ids":["https://openalex.org/I123534392"],"apc_list":null,"apc_paid":null,"fwci":2.2255,"has_fulltext":true,"cited_by_count":19,"citation_normalized_percentile":{"value":0.90781654,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"2357","last_page":"2366"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9995999932289124,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9995999932289124,"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":0.9994999766349792,"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":0.9986000061035156,"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":0.7708290815353394},{"id":"https://openalex.org/keywords/citation","display_name":"Citation","score":0.6676891446113586},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.5369287729263306},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5320611596107483},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.45053738355636597},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4316887855529785},{"id":"https://openalex.org/keywords/order","display_name":"Order (exchange)","score":0.42335280776023865},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.39029109477996826},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.3866675794124603},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.12938454747200012}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7708290815353394},{"id":"https://openalex.org/C2778805511","wikidata":"https://www.wikidata.org/wiki/Q1713","display_name":"Citation","level":2,"score":0.6676891446113586},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.5369287729263306},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5320611596107483},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45053738355636597},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4316887855529785},{"id":"https://openalex.org/C182306322","wikidata":"https://www.wikidata.org/wiki/Q1779371","display_name":"Order (exchange)","level":2,"score":0.42335280776023865},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.39029109477996826},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3866675794124603},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.12938454747200012},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","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/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.18653/v1/d15-1283","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d15-1283","pdf_url":"https://www.aclweb.org/anthology/D15-1283.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},{"id":"pmh:info:ark/67531/metadc991478","is_oa":false,"landing_page_url":"https://digital.library.unt.edu/ark:/67531/metadc991478/","pdf_url":null,"source":{"id":"https://openalex.org/S4306400792","display_name":"University of North Texas Digital Library (University of North Texas)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I123534392","host_organization_name":"University of North Texas","host_organization_lineage":["https://openalex.org/I123534392"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2015 Conference on Empirical Methods in Natural Language Processing, September 17-21, 2015. Lisbon, Portugal","raw_type":"Paper"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.696.3157","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.696.3157","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://aclweb.org/anthology/D/D15/D15-1283.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.721.9369","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.721.9369","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cse.unt.edu/%7Eccaragea/papers/emnlp15.pdf","raw_type":"text"}],"best_oa_location":{"id":"doi:10.18653/v1/d15-1283","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d15-1283","pdf_url":"https://www.aclweb.org/anthology/D15-1283.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.6000000238418579,"display_name":"Quality Education"}],"awards":[{"id":"https://openalex.org/G8654257389","display_name":"III: Small: Collaborative Research: Keyphrase Extraction in Document Networks","funder_award_id":"1423337","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2250924865.pdf","grobid_xml":"https://content.openalex.org/works/W2250924865.grobid-xml"},"referenced_works_count":32,"referenced_works":["https://openalex.org/W62810358","https://openalex.org/W71104953","https://openalex.org/W205532704","https://openalex.org/W1123776667","https://openalex.org/W1532325895","https://openalex.org/W1590650811","https://openalex.org/W1880262756","https://openalex.org/W1908728294","https://openalex.org/W1969629226","https://openalex.org/W2000964648","https://openalex.org/W2006119904","https://openalex.org/W2028842874","https://openalex.org/W2048679005","https://openalex.org/W2049718889","https://openalex.org/W2073769235","https://openalex.org/W2088336913","https://openalex.org/W2097433297","https://openalex.org/W2114535528","https://openalex.org/W2115477592","https://openalex.org/W2129028998","https://openalex.org/W2136504847","https://openalex.org/W2144512097","https://openalex.org/W2147057843","https://openalex.org/W2149684865","https://openalex.org/W2150874198","https://openalex.org/W2157979304","https://openalex.org/W2160992478","https://openalex.org/W2167660864","https://openalex.org/W2171161922","https://openalex.org/W2251476947","https://openalex.org/W4231510805","https://openalex.org/W4239931746"],"related_works":["https://openalex.org/W230091440","https://openalex.org/W2233261550","https://openalex.org/W2810751659","https://openalex.org/W258997015","https://openalex.org/W2997094352","https://openalex.org/W3216976533","https://openalex.org/W100620283","https://openalex.org/W2495260952","https://openalex.org/W4394050964","https://openalex.org/W2551249631"],"abstract_inverted_index":{"With":[0],"the":[1,8,64,79,92,102],"exponential":[2],"growth":[3],"of":[4,26,69,81,104],"scholarly":[5],"data":[6,29,106],"during":[7],"past":[9],"few":[10],"years,":[11],"effective":[12],"methods":[13],"for":[14,108],"topic":[15,80],"classification":[16],"are":[17],"greatly":[18],"needed.":[19],"Current":[20],"approaches":[21],"usually":[22],"require":[23],"large":[24],"amounts":[25],"expensive":[27],"labeled":[28,105],"in":[30,42,101],"order":[31],"to":[32,44,77],"make":[33],"accurate":[34,110],"predictions.":[35],"In":[36],"this":[37,87],"paper,":[38],"we":[39],"posit":[40],"that,":[41],"addition":[43],"a":[45,59,70,98],"research":[46,71],"article's":[47],"textual":[48],"content,":[49],"its":[50],"citation":[51,67],"network":[52],"also":[53,96],"contains":[54],"valuable":[55],"information.":[56],"We":[57,84],"describe":[58],"co-training":[60],"approach":[61],"that":[62,86],"uses":[63],"text":[65],"and":[66],"information":[68],"article":[72],"as":[73],"two":[74],"different":[75],"views":[76],"predict":[78],"an":[82],"article.":[83],"show":[85],"method":[88],"improves":[89],"significantly":[90],"over":[91],"individual":[93],"classifiers,":[94],"while":[95],"bringing":[97],"substantial":[99],"reduction":[100],"amount":[103],"required":[107],"training":[109],"classifiers.":[111]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":1}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}
