{"id":"https://openalex.org/W2964219776","doi":"https://doi.org/10.1145/3331184.3331427","title":"USEing Transfer Learning in Retrieval of Statistical Data","display_name":"USEing Transfer Learning in Retrieval of Statistical Data","publication_year":2019,"publication_date":"2019-07-18","ids":{"openalex":"https://openalex.org/W2964219776","doi":"https://doi.org/10.1145/3331184.3331427","mag":"2964219776"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1145/3331184.3331427","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-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/A5030492823","display_name":"Anton Firsov","orcid":null},"institutions":[],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Anton Firsov","raw_affiliation_strings":["Knoema Corporation, Perm, Russian Fed."],"affiliations":[{"raw_affiliation_string":"Knoema Corporation, Perm, Russian Fed.","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061757942","display_name":"Vladimir Bugay","orcid":null},"institutions":[],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Vladimir Bugay","raw_affiliation_strings":["Knoema Corporation, Perm, Russian Fed."],"affiliations":[{"raw_affiliation_string":"Knoema Corporation, Perm, Russian Fed.","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042013851","display_name":"Anton Karpenko","orcid":null},"institutions":[],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Anton Karpenko","raw_affiliation_strings":["Knoema Corporation, Perm, Russian Fed."],"affiliations":[{"raw_affiliation_string":"Knoema Corporation, Perm, Russian Fed.","institution_ids":[]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.07,"has_fulltext":true,"fulltext_origin":"ngrams","cited_by_count":1,"citation_normalized_percentile":{"value":0.248969,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":62,"max":70},"biblio":{"volume":null,"issue":null,"first_page":"1391","last_page":"1392"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Natural Language Processing","score":0.9997,"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/T10028","display_name":"Natural Language Processing","score":0.9997,"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/T10181","display_name":"Statistical Machine Translation and Natural Language Processing","score":0.9993,"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/T13083","display_name":"Automatic Keyword Extraction from Textual Data","score":0.9924,"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/language-modeling","display_name":"Language Modeling","score":0.554468},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information Retrieval","score":0.547812},{"id":"https://openalex.org/keywords/topic-modeling","display_name":"Topic Modeling","score":0.542489},{"id":"https://openalex.org/keywords/syntax-based-translation-models","display_name":"Syntax-based Translation Models","score":0.542461},{"id":"https://openalex.org/keywords/named-entity-recognition","display_name":"Named Entity Recognition","score":0.541038},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.46582577},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.44113666}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8686187},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.642349},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.58662945},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.55779684},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.54727554},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.4921226},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.46582577},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44745675},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.44113666},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.44069186},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.42081413},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3769033},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.08417189},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1145/3331184.3331427","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.81,"id":"https://metadata.un.org/sdg/4","display_name":"Quality education"}],"grants":[],"datasets":[],"versions":[],"referenced_works_count":2,"referenced_works":["https://openalex.org/W2136189984","https://openalex.org/W2740812102"],"related_works":["https://openalex.org/W4390516098","https://openalex.org/W4287644835","https://openalex.org/W4254087122","https://openalex.org/W3098003361","https://openalex.org/W3092281475","https://openalex.org/W2384362569","https://openalex.org/W2274846632","https://openalex.org/W2181948922","https://openalex.org/W2172090196","https://openalex.org/W2142795561"],"abstract_inverted_index":{"DSSM-like":[0,84],"models":[1,17,46,48,98],"showed":[2],"good":[3],"results":[4,62,73],"in":[5,28,38,99],"retrieval":[6],"of":[7,21,53,59,66,111],"short":[8],"documents":[9],"that":[10,24],"semantically":[11],"match":[12],"the":[13,32,35,41,60],"query.":[14],"However,":[15],"these":[16],"require":[18],"large":[19],"collections":[20],"click-through":[22,112],"data":[23,113],"are":[25],"not":[26],"available":[27],"some":[29],"domains.":[30],"On":[31],"other":[33,67],"hand,":[34],"recent":[36],"advances":[37],"NLP":[39],"demonstrated":[40],"possibility":[42],"to":[43,55,70,101,104,124],"fine-tune":[44,105],"language":[45],"and":[47,91,114],"trained":[49],"on":[50,63,107],"one":[51],"set":[52],"tasks":[54,68],"achieve":[56],"a":[57,64,108],"state":[58],"art":[61],"multitude":[65],"or":[69],"get":[71],"competitive":[72],"using":[74],"much":[75],"smaller":[76],"training":[77],"sets.":[78],"Following":[79],"this":[80],"trend,":[81],"we":[82],"combined":[83],"architecture":[85],"with":[86],"USE":[87],"(Universal":[88],"Sentence":[89],"Encoder)":[90],"BERT":[92],"(Bidirectional":[93],"Encoder":[94],"Representations":[95],"from":[96],"Transformers)":[97],"order":[100],"be":[102],"able":[103],"them":[106,116],"small":[109],"amount":[110],"use":[115],"for":[117,130],"information":[118],"retrieval.":[119],"This":[120],"approach":[121],"allowed":[122],"us":[123],"significantly":[125],"improve":[126],"our":[127],"search":[128],"engine":[129],"statistical":[131],"data.":[132]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W2964219776","counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2024-11-30T14:05:47.665948","created_date":"2019-07-30"}