{"id":"https://openalex.org/W7143661701","doi":"https://doi.org/10.20736/0002002057","title":"TMUNLPG2 at the NTCIR-18 MedNLP-CHAT Task","display_name":"TMUNLPG2 at the NTCIR-18 MedNLP-CHAT Task","publication_year":2025,"publication_date":"2025-06-06","ids":{"openalex":"https://openalex.org/W7143661701","doi":"https://doi.org/10.20736/0002002057"},"language":"en","primary_location":{"id":"pmh:oai:irdb.nii.ac.jp:03100:0006839220","is_oa":true,"landing_page_url":"https://repository.nii.ac.jp/records/2002057","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":"article","indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://repository.nii.ac.jp/records/2002057","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5123827716","display_name":"Pei-Ying Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Pei-Ying Yang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110722324","display_name":"T.-C. Peng","orcid":"https://orcid.org/0009-0001-3647-2718"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tzu-Cheng Peng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035910093","display_name":"Wen-Chao Yeh","orcid":"https://orcid.org/0000-0002-5227-0120"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wen-Chao Yeh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101806372","display_name":"Chien Chin Chen","orcid":"https://orcid.org/0000-0002-8357-1305"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chien Chin Chen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5131220946","display_name":"Yung-Chun Chang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yung-Chun Chang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5123827716"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.85985314,"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/T10028","display_name":"Topic Modeling","score":0.400299996137619,"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":"Topic Modeling","score":0.400299996137619,"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/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.33809998631477356,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.08900000154972076,"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/task","display_name":"Task (project management)","score":0.7648000121116638},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5788000226020813},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"Feature engineering","score":0.5407000184059143},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.4878999888896942},{"id":"https://openalex.org/keywords/task-analysis","display_name":"Task analysis","score":0.4796000123023987},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4108999967575073},{"id":"https://openalex.org/keywords/natural-language-understanding","display_name":"Natural language understanding","score":0.37689998745918274}],"concepts":[{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.7648000121116638},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7560999989509583},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6140000224113464},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5788000226020813},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.5407000184059143},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.4878999888896942},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.4796000123023987},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4733999967575073},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4108999967575073},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.3968000113964081},{"id":"https://openalex.org/C2779439875","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Natural language understanding","level":3,"score":0.37689998745918274},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.3652999997138977},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3562999963760376},{"id":"https://openalex.org/C2776608160","wikidata":"https://www.wikidata.org/wiki/Q4785462","display_name":"Natural (archaeology)","level":2,"score":0.35120001435279846},{"id":"https://openalex.org/C2983448237","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Language understanding","level":2,"score":0.33550000190734863},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3244999945163727},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.29899999499320984},{"id":"https://openalex.org/C56739046","wikidata":"https://www.wikidata.org/wiki/Q192060","display_name":"Knowledge management","level":1,"score":0.2732999920845032},{"id":"https://openalex.org/C197914299","wikidata":"https://www.wikidata.org/wiki/Q18650","display_name":"Semantic memory","level":3,"score":0.25850000977516174},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2538999915122986}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:irdb.nii.ac.jp:03100:0006839220","is_oa":true,"landing_page_url":"https://repository.nii.ac.jp/records/2002057","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:03100:0006839220","is_oa":true,"landing_page_url":"https://repository.nii.ac.jp/records/2002057","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":0.8673792481422424,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"TMUNLPG2":[1,106],"team":[2],"participated":[3],"in":[4,116,124,142],"the":[5,9,28,32,41,47,72,108,117,125,131],"Japanese":[6,33],"subtask":[7],"of":[8,133],"NTCIR-18":[10],"Medical":[11],"Natural":[12],"Language":[13],"Processing":[14],"for":[15],"AI":[16],"Chat":[17],"(MedNLP-CHAT)":[18],"Task.":[19],"This":[20],"paper":[21],"presents":[22],"our":[23,134],"methodological":[24,135],"approach":[25,101],"and":[26,43,63,120,137],"analyzes":[27],"official":[29],"results.":[30],"For":[31,71],"subtask,":[34],"we":[35,50,75],"implemented":[36],"two":[37],"distinct":[38],"methodologies":[39],"addressing":[40],"objective":[42,48,118],"subjective":[44,73,126],"components.":[45],"In":[46],"task,":[49,74],"fine-tuned":[51],"a":[52,95],"pre-trained":[53],"language":[54,146],"model":[55],"enhanced":[56],"with":[57,105],"focal":[58],"loss,":[59],"comprehensive":[60],"feature":[61,78],"engineering,":[62],"strategic":[64],"data":[65],"augmentation":[66],"techniques":[67],"to":[68,81,93],"optimize":[69],"performance.":[70],"developed":[76],"specialized":[77],"engineering":[79],"methods":[80],"extract":[82],"implicit":[83],"semantic":[84],"relationships":[85],"within":[86],"question-answer":[87],"pairs,":[88],"subsequently":[89],"leveraging":[90],"these":[91],"features":[92],"train":[94],"robust":[96],"deep":[97],"learning":[98],"architecture.":[99],"Our":[100],"yielded":[102],"significant":[103],"results,":[104],"achieving":[107],"highest":[109],"average":[110],"F1-score":[111],"among":[112],"seven":[113],"participating":[114],"teams":[115],"task":[119],"securing":[121],"second":[122],"place":[123],"task.":[127],"These":[128],"outcomes":[129],"demonstrate":[130],"efficacy":[132],"framework":[136],"highlight":[138],"its":[139],"potential":[140],"applications":[141],"advancing":[143],"medical":[144],"natural":[145],"processing":[147],"systems.":[148]},"counts_by_year":[],"updated_date":"2026-04-28T14:05:53.105641","created_date":"2026-04-01T00:00:00"}
