{"id":"https://openalex.org/W7225798090","doi":"https://doi.org/10.1038/s41598-026-78042-y","title":"A hybrid deep learning framework with domain-adapted embeddings for regulatory violation classification","display_name":"A hybrid deep learning framework with domain-adapted embeddings for regulatory violation classification","publication_year":2026,"publication_date":"2026-11-11","ids":{"openalex":"https://openalex.org/W7225798090","doi":"https://doi.org/10.1038/s41598-026-78042-y"},"language":"en","primary_location":{"id":"doi:10.1038/s41598-026-78042-y","doi":"https://doi.org/10.1038/s41598-026-78042-y","is_oa":true,"landing_page_url":"https://doi.org/10.1038/s41598-026-78042-y","pdf_url":"https://www.nature.com/articles/s41598-026-78042-y_reference.pdf","source":{"id":"https://openalex.org/S196734849","display_name":"Scientific Reports","issn_l":"2045-2322","issn":["2045-2322"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","dongbi-c","doyens","jufo-1","ki-jl-1","medline","norway-1","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310319908","host_organization_name":"Nature Portfolio","host_organization_lineage":["https://openalex.org/P4310319908","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Nature Portfolio","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Scientific Reports","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://www.nature.com/articles/s41598-026-78042-y_reference.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5161438528","display_name":"Shuai Li","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I109173049","display_name":"Beijing Foreign Studies University","ror":"https://ror.org/00jdr0662","country_code":"CN","type":"education","lineage":["https://openalex.org/I109173049"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shuai Li","raw_affiliation_strings":["Law School, Beijing Foreign Studies University, Beijing, 100089, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Law School, Beijing Foreign Studies University, Beijing, 100089, China","institution_ids":["https://openalex.org/I109173049"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5161438528"],"corresponding_institution_ids":["https://openalex.org/I109173049"],"apc_list":{"value":2690,"currency":"USD","value_usd":2690},"apc_paid":{"value":2690,"currency":"USD","value_usd":2690},"fwci":0.0,"has_fulltext":true,"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":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13643","display_name":"Artificial Intelligence in Law","score":0.492900013923645,"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"}},"topics":[{"id":"https://openalex.org/T13643","display_name":"Artificial Intelligence in Law","score":0.492900013923645,"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":0.42579999566078186,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.061500001698732376,"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":0.7774999737739563},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7210999727249146},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6051999926567078},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6003000140190125},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.593999981880188},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5533000230789185},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5353999733924866},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.5332000255584717},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5322999954223633},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4763999879360199},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.4271000027656555},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.42320001125335693},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3968999981880188},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.37119999527931213},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3693999946117401},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.3603000044822693},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.35100001096725464},{"id":"https://openalex.org/C547195049","wikidata":"https://www.wikidata.org/wiki/Q1725664","display_name":"Terminology","level":2,"score":0.3474999964237213},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.27570000290870667},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2614000141620636}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1038/s41598-026-78042-y","doi":"https://doi.org/10.1038/s41598-026-78042-y","is_oa":true,"landing_page_url":"https://doi.org/10.1038/s41598-026-78042-y","pdf_url":"https://www.nature.com/articles/s41598-026-78042-y_reference.pdf","source":{"id":"https://openalex.org/S196734849","display_name":"Scientific Reports","issn_l":"2045-2322","issn":["2045-2322"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","dongbi-c","doyens","jufo-1","ki-jl-1","medline","norway-1","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310319908","host_organization_name":"Nature Portfolio","host_organization_lineage":["https://openalex.org/P4310319908","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Nature Portfolio","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Scientific Reports","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1038/s41598-026-78042-y","doi":"https://doi.org/10.1038/s41598-026-78042-y","is_oa":true,"landing_page_url":"https://doi.org/10.1038/s41598-026-78042-y","pdf_url":"https://www.nature.com/articles/s41598-026-78042-y_reference.pdf","source":{"id":"https://openalex.org/S196734849","display_name":"Scientific Reports","issn_l":"2045-2322","issn":["2045-2322"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","dongbi-c","doyens","jufo-1","ki-jl-1","medline","norway-1","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310319908","host_organization_name":"Nature Portfolio","host_organization_lineage":["https://openalex.org/P4310319908","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Nature Portfolio","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Scientific Reports","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7225798090.pdf","grobid_xml":"https://content.openalex.org/works/W7225798090.grobid-xml"},"referenced_works_count":3,"referenced_works":["https://openalex.org/W3092129462","https://openalex.org/W3199367723","https://openalex.org/W4221153498"],"related_works":[],"abstract_inverted_index":{"Regulatory":[0],"violation":[1,157,186],"classification":[2,173],"encounters":[3],"fundamental":[4],"challenges":[5],"arising":[6],"from":[7],"the":[8,82,160],"lack":[9],"of":[10,29,143,162,194],"domain-specific":[11],"knowledge":[12],"in":[13,21,171],"general-purpose":[14],"language":[15],"models,":[16],"insufficient":[17],"multi-granular":[18],"feature":[19,54],"representation":[20],"single-architecture":[22],"frameworks,":[23],"and":[24,56,75,99,109,122,133,168,191],"severe":[25],"class":[26],"imbalance":[27],"characteristic":[28],"real-world":[30],"regulatory":[31,182],"datasets.":[32],"This":[33],"study":[34],"introduces":[35],"a":[36,140],"hybrid":[37],"deep":[38],"learning":[39,59,128,170],"framework":[40,176],"integrating":[41],"three":[42],"synergistic":[43],"innovations":[44],"to":[45],"address":[46],"these":[47],"limitations":[48],"through":[49,78,103,125],"domain-adapted":[50,62],"embedding":[51,64],"fusion,":[52],"hierarchical":[53,165],"extraction,":[55],"class-aware":[57,114],"contrastive":[58,115,127,169],"mechanisms.":[60,112],"The":[61,113,175],"dual-layer":[63],"fusion":[65,116,135],"(DA-DLEF)":[66],"dynamically":[67],"combines":[68],"general":[69],"linguistic":[70],"knowledge,":[71],"specialized":[72],"legal":[73],"understanding,":[74],"regulatory-specific":[76],"terminology":[77],"attention-based":[79],"integration,":[80],"while":[81],"Hierarchical":[83],"Hybrid":[84],"Feature":[85],"Extraction":[86],"Network":[87],"(H2-FEN)":[88],"captures":[89],"discriminative":[90],"patterns":[91],"across":[92],"local":[93],"n-gram":[94],"sequences,":[95],"bidirectional":[96],"temporal":[97],"dependencies,":[98],"global":[100],"contextual":[101],"relationships":[102],"multi-scale":[104],"convolutional":[105],"processing,":[106],"recurrent":[107],"modeling,":[108],"regulatory-biased":[110],"self-attention":[111],"classifier":[117],"(CA-CFC)":[118],"optimizes":[119],"inter-class":[120],"separability":[121],"intra-class":[123],"compactness":[124],"supervised":[126],"combined":[129],"with":[130,188],"focal":[131],"loss":[132],"multi-expert":[134],"strategies.":[136],"Comprehensive":[137],"evaluation":[138],"on":[139],"constructed":[141],"dataset":[142],"Chinese":[144],"administrative":[145],"penalty":[146],"decisions":[147],"demonstrates":[148],"substantial":[149],"improvements":[150],"over":[151],"state-of-the-art":[152],"baselines,":[153],"particularly":[154],"for":[155,180],"minority":[156],"categories,":[158],"validating":[159],"effectiveness":[161],"domain":[163],"adaptation,":[164],"architecture":[166],"design,":[167],"enhancing":[172],"performance.":[174],"provides":[177],"practical":[178],"solutions":[179],"governmental":[181],"agencies":[183],"requiring":[184],"automated":[185],"categorization":[187],"enhanced":[189],"accuracy":[190],"equitable":[192],"treatment":[193],"underrepresented":[195],"categories.":[196]},"counts_by_year":[],"updated_date":"2026-11-12T12:28:57.059296","created_date":"2026-11-12T00:00:00"}
