{"id":"https://openalex.org/W7225833817","doi":"https://doi.org/10.1007/s10791-026-10767-w","title":"A semantic quality generation and class-aware contrastive learning network for corporate credit risk prediction","display_name":"A semantic quality generation and class-aware contrastive learning network for corporate credit risk prediction","publication_year":2026,"publication_date":"2026-11-11","ids":{"openalex":"https://openalex.org/W7225833817","doi":"https://doi.org/10.1007/s10791-026-10767-w"},"language":"en","primary_location":{"id":"doi:10.1007/s10791-026-10767-w","doi":"https://doi.org/10.1007/s10791-026-10767-w","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10791-026-10767-w","pdf_url":null,"source":{"id":"https://openalex.org/S5407036663","display_name":"Discover Computing","issn_l":"2948-2992","issn":["2948-2992"],"is_oa":true,"is_in_doaj":true,"is_core":false,"listed_in":["ccf-c","doaj","dongbi-d","jufo-3","norway-2","russia-white-list-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Discover Computing","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1007/s10791-026-10767-w","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5161470327","display_name":"Tingrui Liu","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Tingrui Liu","raw_affiliation_strings":["H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Georgia, 30332, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Georgia, 30332, USA","institution_ids":["https://openalex.org/I130701444"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5161470327"],"corresponding_institution_ids":["https://openalex.org/I130701444"],"apc_list":{"value":1520,"currency":"USD","value_usd":1520},"apc_paid":{"value":1520,"currency":"USD","value_usd":1520},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"29","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1402","display_name":"Accounting"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1402","display_name":"Accounting"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12509","display_name":"Corporate Insolvency and Governance","score":9.999999747378752e-05,"subfield":{"id":"https://openalex.org/subfields/3308","display_name":"Law"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":9.999999747378752e-05,"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":[],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7404999732971191},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.6162999868392944},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5964000225067139},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5047000050544739},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.4977000057697296},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4846999943256378},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.44760000705718994},{"id":"https://openalex.org/C178350159","wikidata":"https://www.wikidata.org/wiki/Q162714","display_name":"Credit risk","level":2,"score":0.44350001215934753},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4133000075817108},{"id":"https://openalex.org/C2777388754","wikidata":"https://www.wikidata.org/wiki/Q1664594","display_name":"Bankruptcy prediction","level":3,"score":0.4065999984741211},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4009000062942505},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.38670000433921814},{"id":"https://openalex.org/C504631918","wikidata":"https://www.wikidata.org/wiki/Q152074","display_name":"Bankruptcy","level":2,"score":0.38029998540878296},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3610000014305115},{"id":"https://openalex.org/C2779806880","wikidata":"https://www.wikidata.org/wiki/Q778470","display_name":"Probability of default","level":3,"score":0.3221000134944916},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2793000042438507},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.2736000120639801},{"id":"https://openalex.org/C76073288","wikidata":"https://www.wikidata.org/wiki/Q1337875","display_name":"Financial risk","level":2,"score":0.2624000012874603},{"id":"https://openalex.org/C2777946921","wikidata":"https://www.wikidata.org/wiki/Q7449044","display_name":"Semantic analysis (machine learning)","level":2,"score":0.26170000433921814},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.25609999895095825},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.25440001487731934}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s10791-026-10767-w","doi":"https://doi.org/10.1007/s10791-026-10767-w","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10791-026-10767-w","pdf_url":null,"source":{"id":"https://openalex.org/S5407036663","display_name":"Discover Computing","issn_l":"2948-2992","issn":["2948-2992"],"is_oa":true,"is_in_doaj":true,"is_core":false,"listed_in":["ccf-c","doaj","dongbi-d","jufo-3","norway-2","russia-white-list-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Discover Computing","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s10791-026-10767-w","doi":"https://doi.org/10.1007/s10791-026-10767-w","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10791-026-10767-w","pdf_url":null,"source":{"id":"https://openalex.org/S5407036663","display_name":"Discover Computing","issn_l":"2948-2992","issn":["2948-2992"],"is_oa":true,"is_in_doaj":true,"is_core":false,"listed_in":["ccf-c","doaj","dongbi-d","jufo-3","norway-2","russia-white-list-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Discover Computing","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W2295598076","https://openalex.org/W2903950532","https://openalex.org/W2963351448","https://openalex.org/W4239510810","https://openalex.org/W4390401711","https://openalex.org/W4394821041","https://openalex.org/W4398762585","https://openalex.org/W4400618090","https://openalex.org/W4401863471","https://openalex.org/W4402540384","https://openalex.org/W4402919126","https://openalex.org/W4403452733","https://openalex.org/W4404024086","https://openalex.org/W4406170795","https://openalex.org/W4406463978","https://openalex.org/W4406602228","https://openalex.org/W4408155438","https://openalex.org/W4409366123","https://openalex.org/W4410199390","https://openalex.org/W4410997834","https://openalex.org/W4411315316","https://openalex.org/W4412456418","https://openalex.org/W4412729725","https://openalex.org/W4412995674","https://openalex.org/W4413021420","https://openalex.org/W4413757392","https://openalex.org/W4414535450","https://openalex.org/W4415455052","https://openalex.org/W4416296524","https://openalex.org/W4416302727","https://openalex.org/W4416324105","https://openalex.org/W4416386291","https://openalex.org/W7155039757","https://openalex.org/W7162541657","https://openalex.org/W7212041715"],"related_works":[],"abstract_inverted_index":{"Abstract":[0],"Corporate":[1],"credit":[2],"risk":[3,11],"prediction":[4,166],"is":[5,97],"an":[6],"important":[7],"task":[8],"in":[9,57,115,140,151,194],"financial":[10,28,39,88,106],"management":[12],"and":[13,29,41,54,90,125,175,197,209,221,245,259],"intelligent":[14],"decision-making,":[15],"aiming":[16],"to":[17,85,132,185,226],"identify":[18],"high-risk":[19,60],"enterprises":[20],"defined":[21],"by":[22],"bankruptcy":[23,165,277],"outcomes":[24],"based":[25],"on":[26,37,161],"corporate":[27,164,276],"operational":[30],"information.":[31],"However,":[32],"existing":[33],"methods":[34],"mostly":[35],"rely":[36],"structured":[38],"indicators":[40],"still":[42],"suffer":[43],"from":[44],"insufficient":[45],"semantic":[46,91,216],"representation,":[47],"uncontrollable":[48],"quality":[49,92,217],"of":[50,104,155],"generated":[51],"textual":[52,102],"descriptions,":[53,89],"limited":[55],"capability":[56],"identifying":[58],"minority":[59],"samples.":[61],"To":[62],"address":[63],"these":[64],"issues,":[65],"this":[66],"paper":[67],"proposes":[68],"SMCCL-Net,":[69],"a":[70,116,270],"framework":[71,268],"that":[72,169,215,232,265],"couples":[73],"semantic-view":[74,143],"reliability":[75],"with":[76,145],"category-aware":[77,222],"cross-modal":[78,126,147,219],"representation":[79,133,210,272],"learning.":[80,134],"Multiple":[81],"prompts":[82],"are":[83,112,191],"used":[84,98],"generate":[86],"candidate":[87,96],"evaluation":[93],"determines":[94],"which":[95],"as":[99],"the":[100,136,152,156,172,178,254,266],"positive":[101],"view":[103],"each":[105],"record.":[107],"The":[108,189],"selected":[109],"tabular-text":[110],"pairs":[111],"then":[113],"optimized":[114],"shared":[117],"latent":[118],"space,":[119],"where":[120],"class":[121],"frequency,":[122],"subclass":[123],"relationships,":[124],"difficulty":[127],"jointly":[128],"determine":[129],"their":[130],"contributions":[131],"Therefore,":[135],"main":[137],"contribution":[138],"lies":[139],"connecting":[141],"quality-controlled":[142],"construction":[144],"minority-class-oriented":[146],"optimization,":[148],"rather":[149,200],"than":[150,201],"isolated":[153],"use":[154],"individual":[157],"techniques.":[158],"Experimental":[159],"results":[160,263],"two":[162],"public":[163],"datasets":[167],"show":[168],"SMCCL-Net":[170],"achieves":[171],"highest":[173],"PR-AUC":[174],"G-mean":[176],"among":[177],"compared":[179],"methods,":[180],"while":[181],"maintaining":[182],"ROC-AUC":[183],"comparable":[184],"strong":[186],"tabular":[187],"baselines.":[188],"gains":[190,258],"therefore":[192],"concentrated":[193],"minority-class":[195],"recognition":[196],"balanced":[198],"classification":[199],"uniform":[202],"improvement":[203],"across":[204],"all":[205],"metrics.":[206],"Ablation":[207],"experiments":[208],"space":[211],"analysis":[212,230],"further":[213],"demonstrate":[214],"filtering,":[218],"alignment,":[220],"mechanisms":[223],"contribute":[224],"positively":[225],"model":[227],"performance.":[228],"Computational":[229],"shows":[231],"online":[233,250],"semantic-enhanced":[234,271],"inference":[235],"requires":[236],"six":[237],"LLM":[238,251],"API":[239],"calls":[240],"per":[241],"sample,":[242],"whereas":[243],"cached":[244],"tabular-only":[246],"modes":[247],"require":[248],"no":[249],"calls,":[252],"highlighting":[253],"trade-off":[255],"between":[256],"predictive":[257],"deployment":[260],"overhead.":[261],"These":[262],"indicate":[264],"proposed":[267],"provides":[269],"learning":[273],"approach":[274],"for":[275],"prediction.":[278]},"counts_by_year":[],"updated_date":"2026-11-12T12:28:57.059296","created_date":"2026-11-12T00:00:00"}
