{"id":"https://openalex.org/W7147354362","doi":"https://doi.org/10.1109/cnml68938.2026.11452416","title":"Contrastive Learning-Based Semantic Recognition Model for Cross-Departmental Documents in Public Organizations","display_name":"Contrastive Learning-Based Semantic Recognition Model for Cross-Departmental Documents in Public Organizations","publication_year":2026,"publication_date":"2026-01-30","ids":{"openalex":"https://openalex.org/W7147354362","doi":"https://doi.org/10.1109/cnml68938.2026.11452416"},"language":null,"primary_location":{"id":"doi:10.1109/cnml68938.2026.11452416","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cnml68938.2026.11452416","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 International Conference on Communication Networks and Machine Learning (CNML)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"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/A5056220719","display_name":"H H Li","orcid":null},"institutions":[{"id":"https://openalex.org/I18452120","display_name":"Yantai University","ror":"https://ror.org/01rp41m56","country_code":"CN","type":"education","lineage":["https://openalex.org/I18452120"]},{"id":"https://openalex.org/I4210108301","display_name":"Yantai Academy of Agricultural Sciences","ror":"https://ror.org/01t81st47","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210108301"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hao Li","raw_affiliation_strings":["China Agricultural University,Yantai Institute,Yantai,China"],"affiliations":[{"raw_affiliation_string":"China Agricultural University,Yantai Institute,Yantai,China","institution_ids":["https://openalex.org/I4210108301","https://openalex.org/I18452120"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132683393","display_name":"Zhilin Guo","orcid":null},"institutions":[{"id":"https://openalex.org/I18452120","display_name":"Yantai University","ror":"https://ror.org/01rp41m56","country_code":"CN","type":"education","lineage":["https://openalex.org/I18452120"]},{"id":"https://openalex.org/I4210108301","display_name":"Yantai Academy of Agricultural Sciences","ror":"https://ror.org/01t81st47","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210108301"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhilin Guo","raw_affiliation_strings":["China Agricultural University,Yantai Institute,Yantai,China"],"affiliations":[{"raw_affiliation_string":"China Agricultural University,Yantai Institute,Yantai,China","institution_ids":["https://openalex.org/I4210108301","https://openalex.org/I18452120"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100764713","display_name":"Zijun Zhang","orcid":"https://orcid.org/0000-0002-2717-5033"},"institutions":[{"id":"https://openalex.org/I18452120","display_name":"Yantai University","ror":"https://ror.org/01rp41m56","country_code":"CN","type":"education","lineage":["https://openalex.org/I18452120"]},{"id":"https://openalex.org/I4210108301","display_name":"Yantai Academy of Agricultural Sciences","ror":"https://ror.org/01t81st47","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210108301"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zijun Zhang","raw_affiliation_strings":["China Agricultural University,Yantai Institute,Yantai,China"],"affiliations":[{"raw_affiliation_string":"China Agricultural University,Yantai Institute,Yantai,China","institution_ids":["https://openalex.org/I4210108301","https://openalex.org/I18452120"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5056220719"],"corresponding_institution_ids":["https://openalex.org/I18452120","https://openalex.org/I4210108301"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.94360389,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"932","last_page":"935"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.1940000057220459,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.1940000057220459,"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/T10953","display_name":"E-Government and Public Services","score":0.1274999976158142,"subfield":{"id":"https://openalex.org/subfields/3320","display_name":"Political Science and International Relations"},"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/T11550","display_name":"Text and Document Classification Technologies","score":0.07079999893903732,"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/government","display_name":"Government (linguistics)","score":0.5300999879837036},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.517799973487854},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5016000270843506},{"id":"https://openalex.org/keywords/terminology","display_name":"Terminology","score":0.4399999976158142},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3774000108242035},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.35839998722076416},{"id":"https://openalex.org/keywords/computational-linguistics","display_name":"Computational linguistics","score":0.3580999970436096},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.3531000018119812}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7961000204086304},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6026999950408936},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5903000235557556},{"id":"https://openalex.org/C2778137410","wikidata":"https://www.wikidata.org/wiki/Q2732820","display_name":"Government (linguistics)","level":2,"score":0.5300999879837036},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.517799973487854},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5016000270843506},{"id":"https://openalex.org/C547195049","wikidata":"https://www.wikidata.org/wiki/Q1725664","display_name":"Terminology","level":2,"score":0.4399999976158142},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3774000108242035},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.35839998722076416},{"id":"https://openalex.org/C155092808","wikidata":"https://www.wikidata.org/wiki/Q182557","display_name":"Computational linguistics","level":2,"score":0.3580999970436096},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3531000018119812},{"id":"https://openalex.org/C2780876879","wikidata":"https://www.wikidata.org/wiki/Q3054749","display_name":"Meaning (existential)","level":2,"score":0.3418999910354614},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3395000100135803},{"id":"https://openalex.org/C85407183","wikidata":"https://www.wikidata.org/wiki/Q1045785","display_name":"Semantic network","level":2,"score":0.3305000066757202},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.3287000060081482},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3260999917984009},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.322299987077713},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.29840001463890076},{"id":"https://openalex.org/C2777629044","wikidata":"https://www.wikidata.org/wiki/Q614959","display_name":"Contrastive analysis","level":2,"score":0.29760000109672546},{"id":"https://openalex.org/C2777462759","wikidata":"https://www.wikidata.org/wiki/Q18395344","display_name":"Word embedding","level":3,"score":0.2971000075340271},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.28220000863075256},{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.2761000096797943},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27300000190734863},{"id":"https://openalex.org/C198942812","wikidata":"https://www.wikidata.org/wiki/Q496618","display_name":"Semantic property","level":2,"score":0.2651999890804291},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.25450000166893005}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cnml68938.2026.11452416","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cnml68938.2026.11452416","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 International Conference on Communication Networks and Machine Learning (CNML)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W3038445625","https://openalex.org/W4312408631","https://openalex.org/W4386324533","https://openalex.org/W4391609691","https://openalex.org/W4392203438","https://openalex.org/W4399914009","https://openalex.org/W4400156412","https://openalex.org/W4402683969","https://openalex.org/W4402696041","https://openalex.org/W7131839702"],"related_works":[],"abstract_inverted_index":{"Cross-departmental":[0],"collaborative":[1],"governance":[2],"in":[3],"modern":[4],"digital":[5],"government":[6,42],"faces":[7],"bottlenecks":[8],"including":[9],"terminology":[10],"islands,":[11],"administrative":[12],"caliber":[13],"drift,":[14],"and":[15,72,75,109,112,133,145],"implicit":[16],"logical":[17],"conflicts.":[18],"To":[19],"address":[20],"these":[21],"issues,":[22],"we":[23,99],"propose":[24],"MGCF-Gov,":[25],"a":[26,48,56,60,77],"multi-granularity":[27],"contrastive":[28,92],"fusion":[29],"network":[30],"that":[31,51,66,83,126],"learns":[32],"an":[33,129,134],"isomorphic":[34],"mapping":[35],"from":[36,96],"unstructured":[37],"public":[38],"appeals":[39],"to":[40],"structured":[41],"rules.":[43],"The":[44],"framework":[45],"integrates":[46],"(i)":[47],"prompt-driven":[49],"encoder":[50],"injects":[52],"administrative-domain":[53],"priors":[54],"into":[55],"pre-trained":[57],"backbone,":[58],"(ii)":[59],"Multi-granularity":[61],"Contrastive":[62],"Learning":[63],"(MGCL)":[64],"objective":[65],"aligns":[67],"representations":[68],"at":[69],"both":[70],"macro-intent":[71],"micro-terminology":[73],"levels,":[74],"(iii)":[76],"Dynamic":[78],"Semantic":[79],"Fusion":[80],"(DSF)":[81],"module":[82],"performs":[84],"cross-source":[85],"evidence":[86],"aggregation":[87],"via":[88],"attention-based":[89],"fusion.":[90],"Since":[91],"objectives":[93],"can":[94],"suffer":[95],"representation":[97],"collapse,":[98],"introduce":[100],"explicit":[101],"collapse":[102,117],"diagnostics":[103],"(e.g.,":[104],"embedding":[105],"variance,":[106],"effective":[107],"rank,":[108],"alignment/uniformity":[110],"metrics)":[111],"analyze":[113],"how":[114],"MGCL":[115],"mitigates":[116],"risk.":[118],"Experiments":[119],"on":[120],"MGCF-Gov-Set":[121],"(750,000":[122],"real-world":[123],"samples)":[124],"show":[125],"MGCF-Gov":[127],"achieves":[128],"F1-score":[130],"of":[131,136],"0.832":[132],"AUC":[135],"0.944,":[137],"outperforming":[138],"strong":[139],"neural":[140],"baselines":[141],"such":[142],"as":[143],"RoBERTa-wwm":[144],"RGCN":[146],"under":[147],"the":[148],"same":[149],"evaluation":[150],"protocol.":[151]},"counts_by_year":[],"updated_date":"2026-04-02T13:53:19.096889","created_date":"2026-04-02T00:00:00"}
