{"id":"https://openalex.org/W7143751574","doi":"https://doi.org/10.20736/0002001295","title":"HPIDHC at NTCIR-17 MedNLP-SC: Data Augmentation and Ensemble Learning for Multilingual Adverse Drug Event Detection","display_name":"HPIDHC at NTCIR-17 MedNLP-SC: Data Augmentation and Ensemble Learning for Multilingual Adverse Drug Event Detection","publication_year":2023,"publication_date":"2023-12-12","ids":{"openalex":"https://openalex.org/W7143751574","doi":"https://doi.org/10.20736/0002001295"},"language":"en","primary_location":{"id":"pmh:oai:irdb.nii.ac.jp:03100:0005993269","is_oa":true,"landing_page_url":"https://repository.nii.ac.jp/records/2001295","pdf_url":null,"source":{"id":"https://openalex.org/S7407056385","display_name":"Institutional Repositories DataBase (IRDB)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I184597095","host_organization_name":"National Institute of Informatics","host_organization_lineage":["https://openalex.org/I184597095"],"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":"conference paper"},"type":"conference-paper","indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://repository.nii.ac.jp/records/2001295","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5131436964","display_name":"Smilla Fox","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Smilla Fox","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5131090026","display_name":"Martin Prei?","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Martin Prei?","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006643663","display_name":"Florian Borchert","orcid":"https://orcid.org/0000-0003-1079-6500"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Florian Borchert","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5131137353","display_name":"Aadil Rasheed","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Aadil Rasheed","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5053012847","display_name":"Matthieu-P. Schapranow","orcid":"https://orcid.org/0000-0001-6601-2942"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Matthieu-P. Schapranow","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.74698795,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"none","last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11943","display_name":"Pharmacovigilance and Adverse Drug Reactions","score":0.3700000047683716,"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":0.3700000047683716,"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/T10028","display_name":"Topic Modeling","score":0.24289999902248383,"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/T11147","display_name":"Misinformation and Its Impacts","score":0.06669999659061432,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.7669000029563904},{"id":"https://openalex.org/keywords/german","display_name":"German","score":0.6902999877929688},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6437000036239624},{"id":"https://openalex.org/keywords/social-media","display_name":"Social media","score":0.5515999794006348},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.531000018119812},{"id":"https://openalex.org/keywords/ensemble-forecasting","display_name":"Ensemble forecasting","score":0.4408000111579895},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.3919999897480011},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.37529999017715454}],"concepts":[{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.7669000029563904},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7372999787330627},{"id":"https://openalex.org/C154775046","wikidata":"https://www.wikidata.org/wiki/Q188","display_name":"German","level":2,"score":0.6902999877929688},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6499999761581421},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6437000036239624},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.609499990940094},{"id":"https://openalex.org/C518677369","wikidata":"https://www.wikidata.org/wiki/Q202833","display_name":"Social media","level":2,"score":0.5515999794006348},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.531000018119812},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5163000226020813},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.4408000111579895},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.3919999897480011},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.37529999017715454},{"id":"https://openalex.org/C89992363","wikidata":"https://www.wikidata.org/wiki/Q5961558","display_name":"Track (disk drive)","level":2,"score":0.34950000047683716},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.30059999227523804},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.29190000891685486},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2822999954223633},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2818000018596649},{"id":"https://openalex.org/C2987896495","wikidata":"https://www.wikidata.org/wiki/Q5416716","display_name":"Event data","level":3,"score":0.26899999380111694},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.266400009393692},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.2630999982357025},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.26190000772476196},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.25099998712539673}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:irdb.nii.ac.jp:03100:0005993269","is_oa":true,"landing_page_url":"https://repository.nii.ac.jp/records/2001295","pdf_url":null,"source":{"id":"https://openalex.org/S7407056385","display_name":"Institutional Repositories DataBase (IRDB)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I184597095","host_organization_name":"National Institute of Informatics","host_organization_lineage":["https://openalex.org/I184597095"],"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":"conference paper"}],"best_oa_location":{"id":"pmh:oai:irdb.nii.ac.jp:03100:0005993269","is_oa":true,"landing_page_url":"https://repository.nii.ac.jp/records/2001295","pdf_url":null,"source":{"id":"https://openalex.org/S7407056385","display_name":"Institutional Repositories DataBase (IRDB)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I184597095","host_organization_name":"National Institute of Informatics","host_organization_lineage":["https://openalex.org/I184597095"],"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":"conference paper"},"sustainable_development_goals":[{"score":0.7460395097732544,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0,89],"Social":[1],"Media":[2],"Adverse":[3],"Drug":[4],"Event":[5],"Detection":[6],"(SM-ADE)":[7],"track":[8],"of":[9,37,91,98],"the":[10,41,53,74,80,87,111,134],"NTCIR-17":[11],"MedNLP-SC":[12],"shared":[13,42],"task":[14],"aims":[15],"to":[16,86],"identify":[17],"adverse":[18],"drug":[19],"events":[20],"(ADE)":[21],"in":[22,110],"Japanese,":[23],"English,":[24],"French,":[25],"and":[26,63,122,124,130],"German":[27,81],"social":[28],"media":[29],"texts.In":[30],"this":[31],"paper,":[32],"we":[33,48,59,71,106],"describe":[34],"selected":[35],"details":[36],"our":[38],"contribution":[39],"addressing":[40],"task.":[43],"As":[44],"a":[45,95],"base":[46],"model,":[47],"fine-tune":[49],"RoBERTa":[50,102],"models":[51],"for":[52],"different":[54],"language":[55],"subtasks.":[56],"In":[57],"addition,":[58],"apply":[60],"ensemble":[61,92],"learning":[62,93],"data":[64,69],"augmentation":[65],"techniques.":[66],"By":[67],"leveraging":[68],"augmentation,":[70],"successfully":[72],"elevate":[73],"resulting":[75],"micro-averaged":[76],"F1":[77,126],"scores":[78,119,127],"on":[79],"dataset":[82],"by":[83],"5pp":[84],"compared":[85],"baseline.":[88],"application":[90],"yields":[94],"remarkable":[96],"improvement":[97],"7pp.":[99],"Through":[100],"combining":[101],"with":[103],"these":[104],"methods,":[105],"achieve":[107],"promising":[108],"results":[109,136],"challenge.":[112],"Our":[113],"best":[114],"runs":[115],"accomplish":[116],"exact":[117],"accuracy":[118],"between":[120,128],"0.84":[121],"0.87":[123],"per-class":[125],"0.77":[129],"0.82,":[131],"consistently":[132],"achieving":[133],"second-best":[135],"across":[137],"all":[138],"languages.":[139]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-04-01T00:00:00"}
