{"id":"https://openalex.org/W4380876073","doi":"https://doi.org/10.3233/jifs-230528","title":"The use of thematic context-based deep learning in discourse expression of sports news","display_name":"The use of thematic context-based deep learning in discourse expression of sports news","publication_year":2023,"publication_date":"2023-06-16","ids":{"openalex":"https://openalex.org/W4380876073","doi":"https://doi.org/10.3233/jifs-230528"},"language":"en","primary_location":{"id":"doi:10.3233/jifs-230528","is_oa":false,"landing_page_url":"https://doi.org/10.3233/jifs-230528","pdf_url":null,"source":{"id":"https://openalex.org/S179157397","display_name":"Journal of Intelligent & Fuzzy Systems","issn_l":"1064-1246","issn":["1064-1246","1875-8967"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Intelligent &amp; Fuzzy Systems","raw_type":"journal-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/A5039789343","display_name":"Yefei Liu","orcid":"https://orcid.org/0000-0003-3233-5801"},"institutions":[{"id":"https://openalex.org/I4210161995","display_name":"Yulin University","ror":"https://ror.org/05rp1t554","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210161995"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yefei Liu","raw_affiliation_strings":["School of Physical Education, Yulin University, Yulin, Shaanxi, China"],"affiliations":[{"raw_affiliation_string":"School of Physical Education, Yulin University, Yulin, Shaanxi, China","institution_ids":["https://openalex.org/I4210161995"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5039789343"],"corresponding_institution_ids":["https://openalex.org/I4210161995"],"apc_list":null,"apc_paid":null,"fwci":0.1097,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.38548935,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"45","issue":"5","first_page":"7271","last_page":"7283"},"is_retracted":true,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12740","display_name":"Gait Recognition and Analysis","score":0.9860000014305115,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12740","display_name":"Gait Recognition and Analysis","score":0.9860000014305115,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.972599983215332,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T13310","display_name":"Subtitles and Audiovisual Media","score":0.9624000191688538,"subfield":{"id":"https://openalex.org/subfields/1203","display_name":"Language and Linguistics"},"field":{"id":"https://openalex.org/fields/12","display_name":"Arts and Humanities"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.795170783996582},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.6181867718696594},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5955109000205994},{"id":"https://openalex.org/keywords/vocabulary","display_name":"Vocabulary","score":0.5890308618545532},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5667518377304077},{"id":"https://openalex.org/keywords/sign-language","display_name":"Sign language","score":0.542446494102478},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49175480008125305},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.4714568257331848},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4426463842391968},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.42476311326026917},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.41339945793151855},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.20441588759422302}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.795170783996582},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.6181867718696594},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5955109000205994},{"id":"https://openalex.org/C2777601683","wikidata":"https://www.wikidata.org/wiki/Q6499736","display_name":"Vocabulary","level":2,"score":0.5890308618545532},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5667518377304077},{"id":"https://openalex.org/C522192633","wikidata":"https://www.wikidata.org/wiki/Q34228","display_name":"Sign language","level":2,"score":0.542446494102478},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49175480008125305},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.4714568257331848},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4426463842391968},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.42476311326026917},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.41339945793151855},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.20441588759422302},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3233/jifs-230528","is_oa":false,"landing_page_url":"https://doi.org/10.3233/jifs-230528","pdf_url":null,"source":{"id":"https://openalex.org/S179157397","display_name":"Journal of Intelligent & Fuzzy Systems","issn_l":"1064-1246","issn":["1064-1246","1875-8967"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Intelligent &amp; Fuzzy Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8899999856948853,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W2472748520","https://openalex.org/W2573587735","https://openalex.org/W2597866042","https://openalex.org/W2760665227","https://openalex.org/W2774362443","https://openalex.org/W2802894142","https://openalex.org/W2885195348","https://openalex.org/W2904745521","https://openalex.org/W3008469982","https://openalex.org/W3021538729","https://openalex.org/W3038273852","https://openalex.org/W3039588553","https://openalex.org/W3039610325","https://openalex.org/W3041279471","https://openalex.org/W3090767198","https://openalex.org/W3117337440","https://openalex.org/W3173101796","https://openalex.org/W3192747194","https://openalex.org/W3197551989","https://openalex.org/W3199320631","https://openalex.org/W3201246529","https://openalex.org/W3201994665","https://openalex.org/W3206719321","https://openalex.org/W3217375558","https://openalex.org/W4220969342","https://openalex.org/W4293029376"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W2349784553","https://openalex.org/W3022596247","https://openalex.org/W2601444686","https://openalex.org/W4294690766","https://openalex.org/W4292238148","https://openalex.org/W2611989081","https://openalex.org/W4379621602","https://openalex.org/W2159815235","https://openalex.org/W2017877785"],"abstract_inverted_index":{"Sports":[0],"news":[1,85,252],"is":[2,8,19,35,73,93,105,199,235],"a":[3,11,36,181],"type":[4],"of":[5,49,56,62,121,131,143,154,185,191,211,229],"discourse":[6,54],"that":[7,39,140],"characterized":[9],"by":[10],"specific":[12],"vocabulary,":[13],"style,":[14],"and":[15,17,29,45,65,129,146,162,176,195,201,217,233,254],"tone,":[16],"it":[18],"typically":[20],"focused":[21],"on":[22,70],"conveying":[23],"information":[24],"about":[25],"sporting":[26],"events,":[27],"athletes,":[28],"teams.":[30],"Thematic":[31],"context-based":[32],"deep":[33,71],"learning":[34,72],"powerful":[37],"approach":[38],"can":[40,219,240],"be":[41,171],"used":[42,106],"to":[43,75,81,107,117,170,244],"analyze":[44],"interpret":[46],"various":[47],"forms":[48],"natural":[50],"language,":[51],"including":[52],"the":[53,88,119,125,132,141,152,157,163,208,226,247],"expression":[55],"sports":[57,84,251],"news.":[58],"An":[59],"application":[60],"model":[61,133,161,167],"sign":[63,122,164,186,192,205],"language":[64,67,90,159,165,187,193,197,206,224],"lip":[66,89,109,158,196,223],"recognition":[68,91,160,166,194,198,209,227],"based":[69],"proposed":[74],"facilitate":[76,241],"people":[77,243],"with":[78,100,151],"hearing":[79],"impairment":[80],"easily":[82],"obtain":[83,246],"content.":[86],"First,":[87],"system":[92],"constructed;":[94],"next,":[95],"MobileNet":[96],"lightweight":[97],"network":[98,114],"combined":[99],"Long-Short":[101],"Term":[102],"Memory":[103],"(LSTM)":[104],"extract":[108,118],"reading":[110],"features.":[111],"ResNet-50":[112],"residual":[113],"structure":[115],"isadopted":[116],"features":[120],"language;":[123],"finally,":[124],"convergence,":[126],"accuracy,":[127],"precision":[128],"recall":[130],"are":[134],"verified":[135],"respectively.":[136,203],"The":[137,189],"results":[138],"show":[139],"loss":[142],"training":[144],"set":[145,148],"test":[147],"converges":[149],"gradually":[150],"increase":[153],"iteration":[155],"times;":[156],"basically":[168],"tend":[169],"stable":[172],"after":[173],"14":[174],"iterations":[175],"12":[177],"iterations,":[178],"respectively,":[179],"suggesting":[180],"better":[182],"convergence":[183],"effect":[184],"recognition.":[188],"accuracy":[190,210,228],"98.9%":[200],"87.7%,":[202],"In":[204,222],"recognition,":[207,225],"numbers":[212,230],"1,":[213],"2,":[214,231],"4,":[215],"6":[216],"8":[218],"reach":[220],"100%.":[221],"3":[232],"9":[234],"relatively":[236],"higher.":[237],"This":[238],"exploration":[239],"hearing-impaired":[242],"quickly":[245],"relevant":[248],"content":[249],"in":[250],"videos,":[253],"also":[255],"provide":[256],"help":[257],"for":[258],"their":[259],"communication.":[260]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
