{"id":"https://openalex.org/W3120525027","doi":null,"title":"KFU NLP Team at SMM4H 2020 Tasks: Cross-lingual Transfer Learning with Pretrained Language Models for Drug Reactions","display_name":"KFU NLP Team at SMM4H 2020 Tasks: Cross-lingual Transfer Learning with Pretrained Language Models for Drug Reactions","publication_year":2020,"publication_date":"2020-12-01","ids":{"openalex":"https://openalex.org/W3120525027","mag":"3120525027"},"language":"en","primary_location":{"id":"mag:3120525027","doi":null,"is_oa":false,"landing_page_url":"https://www.aclweb.org/anthology/2020.smm4h-1.8.pdf","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"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/A5078878309","display_name":"Zulfat Miftahutdinov","orcid":"https://orcid.org/0000-0002-8467-4824","observed_orcids":["https://orcid.org/0000-0002-8467-4824"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zulfat Miftahutdinov","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018477452","display_name":"Andrey Sakhovskiy","orcid":"https://orcid.org/0000-0003-2762-2910","observed_orcids":["https://orcid.org/0000-0003-2762-2910"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Andrey Sakhovskiy","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0003-2762-2910","affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5012311258","display_name":"Elena Viktorovna Tutubalina","orcid":"https://orcid.org/0000-0001-7936-0284","observed_orcids":["https://orcid.org/0000-0001-7936-0284"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Elena Tutubalina","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0001-7936-0284","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.5658,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.77937678,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"51","last_page":"56"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11943","display_name":"Pharmacovigilance and Adverse Drug Reactions","score":0.8476999998092651,"subfield":{"id":"https://openalex.org/subfields/3005","display_name":"Toxicology"},"field":{"id":"https://openalex.org/fields/30","display_name":"Pharmacology, Toxicology and Pharmaceutics"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T11943","display_name":"Pharmacovigilance and Adverse Drug Reactions","score":0.8476999998092651,"subfield":{"id":"https://openalex.org/subfields/3005","display_name":"Toxicology"},"field":{"id":"https://openalex.org/fields/30","display_name":"Pharmacology, Toxicology and Pharmaceutics"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.059300001710653305,"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":0.04699999839067459,"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/adverse-drug-reactions","display_name":"adverse drug reactions","score":0.9300000071525574},{"id":"https://openalex.org/keywords/cross-lingual-transfer-learning","display_name":"cross-lingual transfer learning","score":0.8799999952316284},{"id":"https://openalex.org/keywords/bert","display_name":"BERT","score":0.7360000014305115},{"id":"https://openalex.org/keywords/multilingual-pretrained-language-models","display_name":"multilingual pretrained language models","score":0.7549999952316284},{"id":"https://openalex.org/keywords/social-media-mining-for-health","display_name":"social media mining for health","score":0.7680000066757202},{"id":"https://openalex.org/keywords/tweet-classification","display_name":"tweet classification","score":0.7379999756813049},{"id":"https://openalex.org/keywords/english-russian-comparison","display_name":"English-Russian comparison","score":0.453000009059906}],"concepts":[{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.707876443862915},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6844251155853271},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6793939471244812},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6275888681411743},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.6203503012657166},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.602871298789978},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.4896267056465149},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.41852471232414246},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3421281576156616},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C19165224","wikidata":"https://www.wikidata.org/wiki/Q23404","display_name":"Anthropology","level":1,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:3120525027","doi":null,"is_oa":false,"landing_page_url":"https://www.aclweb.org/anthology/2020.smm4h-1.8.pdf","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.8560000061988831,"display_name":"Good health and well-being"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1832693441","https://openalex.org/W2085752173","https://openalex.org/W2112796928","https://openalex.org/W2131546905","https://openalex.org/W2143017621","https://openalex.org/W2159583324","https://openalex.org/W2271840356","https://openalex.org/W2493916176","https://openalex.org/W2893928366","https://openalex.org/W2907279416","https://openalex.org/W2911489562","https://openalex.org/W2963341956","https://openalex.org/W2964121744","https://openalex.org/W2970641574","https://openalex.org/W2973038827","https://openalex.org/W3015347994","https://openalex.org/W3118356387"],"related_works":["https://openalex.org/W3172866177","https://openalex.org/W2973038827","https://openalex.org/W3119371101","https://openalex.org/W3118456553","https://openalex.org/W3119868278","https://openalex.org/W3118463775","https://openalex.org/W7166890064","https://openalex.org/W3118714446","https://openalex.org/W2972829713","https://openalex.org/W3153354708"],"abstract_inverted_index":{"This":[0],"paper":[1],"describes":[2],"neural":[3,51],"models":[4],"developed":[5],"for":[6,11,53,83],"the":[7,25,67,106],"Social":[8],"Media":[9],"Mining":[10],"Health":[12],"(SMM4H)":[13],"2020":[14,69],"shared":[15],"tasks.":[16,22],"Specifically,":[17],"we":[18],"participated":[19],"in":[20,44],"two":[21,102],"We":[23],"investigate":[24],"use":[26],"of":[27,38,50,57,77,85,105,116],"a":[28,35,74,113],"language":[29],"representation":[30],"model":[31,82],"BERT":[32],"pretrained":[33],"on":[34,118,123],"large-scale":[36],"corpus":[37],"5":[39],"million":[40],"health-related":[41],"user":[42],"reviews":[43],"English":[45,86,119],"and":[46,55,72,87,98,111,121],"Russian.":[47],"The":[48,79],"ensemble":[49],"networks":[52],"extraction":[54],"normalization":[56],"adverse":[58,92],"drug":[59],"reactions":[60,93],"ranked":[61,94],"first":[62,103],"among":[63,96],"7":[64,99],"teams":[65,100],"at":[66,101],"SMM4H":[68,107],"Task":[70,109],"3":[71],"obtained":[73,112],"relaxed":[75,114],"F1":[76,115],"46%.":[78],"BERT-based":[80],"multilingual":[81],"classification":[84],"Russian":[88,124],"tweets":[89,120],"that":[90],"report":[91],"second":[95],"16":[97],"subtasks":[104],"2019":[108],"2":[110],"58%":[117],"51%":[122],"tweets.":[125]},"counts_by_year":[{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":1}],"updated_date":"2026-09-22T06:50:58.402377","created_date":"2021-01-18T00:00:00"}
