{"id":"https://openalex.org/W4207064710","doi":"https://doi.org/10.1109/ssci50451.2021.9660132","title":"Efficient Multilingual Deep Learning Model for Keyword Categorization","display_name":"Efficient Multilingual Deep Learning Model for Keyword Categorization","publication_year":2021,"publication_date":"2021-12-05","ids":{"openalex":"https://openalex.org/W4207064710","doi":"https://doi.org/10.1109/ssci50451.2021.9660132"},"language":"en","primary_location":{"id":"doi:10.1109/ssci50451.2021.9660132","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssci50451.2021.9660132","pdf_url":null,"source":{"id":"https://openalex.org/S4363604921","display_name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","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/A5009738158","display_name":"Mirko Polato","orcid":"https://orcid.org/0000-0003-4890-5020"},"institutions":[{"id":"https://openalex.org/I55143463","display_name":"University of Turin","ror":"https://ror.org/048tbm396","country_code":"IT","type":"education","lineage":["https://openalex.org/I55143463"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Mirko Polato","raw_affiliation_strings":["University of Turin, Turin, Italy"],"affiliations":[{"raw_affiliation_string":"University of Turin, Turin, Italy","institution_ids":["https://openalex.org/I55143463"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091042155","display_name":"Denys Demchenko","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Denys Demchenko","raw_affiliation_strings":["ID Ward, Nottingham, UK"],"affiliations":[{"raw_affiliation_string":"ID Ward, Nottingham, UK","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052709486","display_name":"Almat Kuanyshkereyev","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Almat Kuanyshkereyev","raw_affiliation_strings":["ID Ward, Nottingham, UK"],"affiliations":[{"raw_affiliation_string":"ID Ward, Nottingham, UK","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5088959189","display_name":"Nicol\u00f2 Navarin","orcid":"https://orcid.org/0000-0002-4108-1754"},"institutions":[{"id":"https://openalex.org/I138689650","display_name":"University of Padua","ror":"https://ror.org/00240q980","country_code":"IT","type":"education","lineage":["https://openalex.org/I138689650"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Nicolo Navarin","raw_affiliation_strings":["University of Padua, Padua, Italy"],"affiliations":[{"raw_affiliation_string":"University of Padua, Padua, Italy","institution_ids":["https://openalex.org/I138689650"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5009738158"],"corresponding_institution_ids":["https://openalex.org/I55143463"],"apc_list":null,"apc_paid":null,"fwci":0.1257,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.40848757,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"2017 decem","issue":null,"first_page":"01","last_page":"08"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9997000098228455,"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":0.9997000098228455,"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"}},{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9980999827384949,"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/categorization","display_name":"Categorization","score":0.8590211868286133},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.827971339225769},{"id":"https://openalex.org/keywords/word-embedding","display_name":"Word embedding","score":0.5498747825622559},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5415239334106445},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.5272185802459717},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.48494794964790344},{"id":"https://openalex.org/keywords/product","display_name":"Product (mathematics)","score":0.479314923286438},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.4633031487464905},{"id":"https://openalex.org/keywords/text-categorization","display_name":"Text categorization","score":0.4538312554359436},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.4507916569709778},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.4469812214374542},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4456843137741089},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3386916518211365}],"concepts":[{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.8590211868286133},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.827971339225769},{"id":"https://openalex.org/C2777462759","wikidata":"https://www.wikidata.org/wiki/Q18395344","display_name":"Word embedding","level":3,"score":0.5498747825622559},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5415239334106445},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.5272185802459717},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.48494794964790344},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.479314923286438},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.4633031487464905},{"id":"https://openalex.org/C2986744138","wikidata":"https://www.wikidata.org/wiki/Q302088","display_name":"Text categorization","level":3,"score":0.4538312554359436},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.4507916569709778},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4469812214374542},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4456843137741089},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3386916518211365},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"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/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/ssci50451.2021.9660132","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssci50451.2021.9660132","pdf_url":null,"source":{"id":"https://openalex.org/S4363604921","display_name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","raw_type":"proceedings-article"},{"id":"pmh:oai:www.research.unipd.it:11577/3440098","is_oa":false,"landing_page_url":"http://hdl.handle.net/11577/3440098","pdf_url":null,"source":{"id":"https://openalex.org/S4377196283","display_name":"Research Padua  Archive (University of Padua)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I138689650","host_organization_name":"University of Padua","host_organization_lineage":["https://openalex.org/I138689650"],"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":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W2057069782","https://openalex.org/W2064675550","https://openalex.org/W2125031621","https://openalex.org/W2126725946","https://openalex.org/W2150355110","https://openalex.org/W2250539671","https://openalex.org/W2252212383","https://openalex.org/W2294865516","https://openalex.org/W2493916176","https://openalex.org/W2626967530","https://openalex.org/W2896457183","https://openalex.org/W2952037945","https://openalex.org/W2953273646","https://openalex.org/W2963002901","https://openalex.org/W2963674330","https://openalex.org/W2976348667","https://openalex.org/W4294170691","https://openalex.org/W4297801177","https://openalex.org/W4298393544","https://openalex.org/W4299579390","https://openalex.org/W4385245566","https://openalex.org/W6638665372","https://openalex.org/W6678885109","https://openalex.org/W6682691769","https://openalex.org/W6691929452","https://openalex.org/W6697600467","https://openalex.org/W6739651123","https://openalex.org/W6739901393","https://openalex.org/W6745388339","https://openalex.org/W6748304040","https://openalex.org/W6755207826","https://openalex.org/W6755477022","https://openalex.org/W6765510844"],"related_works":["https://openalex.org/W2360898036","https://openalex.org/W2390857744","https://openalex.org/W2133651098","https://openalex.org/W2390698788","https://openalex.org/W2078570174","https://openalex.org/W2383063829","https://openalex.org/W2138922887","https://openalex.org/W2082678934","https://openalex.org/W2035261173","https://openalex.org/W2106892947"],"abstract_inverted_index":{"Keywords":[0,16],"categorization":[1,36],"is":[2,84],"an":[3],"essential":[4],"tool":[5],"for":[6,69],"SEO":[7],"(Search":[8],"Engine":[9],"Optimization),":[10],"digital":[11],"marketers,":[12],"and":[13,31,49,81],"online":[14],"advertising.":[15],"represent":[17],"one":[18],"of":[19,24,42,93,111,135],"the":[20,28,46,87,99,107,112,122,126,129,133,152],"most":[21],"valuable":[22],"pieces":[23],"information":[25],"to":[26,124,128],"infer":[27],"users'":[29],"intents":[30],"interests.":[32],"An":[33],"effective":[34],"keyword":[35,71],"method":[37],"allows":[38],"understanding":[39],"what":[40],"types":[41],"content":[43,54],"are":[44,149],"in":[45,98],"greatest":[47],"demand":[48],"can":[50,163],"help":[51],"improve":[52],"future":[53],"strategies":[55],"or":[56],"marketing/ad":[57],"campaigns.":[58],"In":[59],"this":[60],"paper,":[61],"we":[62,103,116],"present":[63],"a":[64,139,144],"novel":[65],"deep":[66],"learning":[67],"model":[68,74,162],"multilingual":[70,78],"categorization.":[72],"The":[73],"relies":[75],"on":[76,138],"fastText":[77,101],"word":[79],"embeddings,":[80,102],"its":[82],"architecture":[83],"inspired":[85],"by":[86,120,143],"DeepSets":[88],"model.":[89],"To":[90],"make":[91],"use":[92],"(training)":[94],"words":[95],"not":[96],"included":[97],"pre-trained":[100],"initialize":[104],"them":[105],"as":[106],"average":[108],"embedding":[109],"overall":[110],"co-occurrent":[113],"words.":[114],"Then,":[115],"fine-tune":[117],"these":[118],"representations":[119],"allowing":[121],"network":[123],"back-propagate":[125],"error":[127],"input.":[130],"We":[131],"assess":[132],"quality":[134],"our":[136,161],"proposal":[137],"real-world":[140],"dataset":[141],"provided":[142],"Spanish":[145],"company":[146],"where":[147],"keywords":[148],"categorized":[150],"upon":[151],"Google":[153],"Product":[154],"Taxonomy":[155],"(GPT).":[156],"Empirical":[157],"results":[158],"show":[159],"that":[160],"achieve":[164],"high":[165],"accuracy":[166],"scores":[167],"while":[168],"being":[169],"extremely":[170],"efficient.":[171]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2022-01-26T00:00:00"}
