{"id":"https://openalex.org/W4402712972","doi":"https://doi.org/10.1145/3653644.3665180","title":"Application of A Question Recognition Strategy for Small Data Sets in Medical Insurance QA Systems","display_name":"Application of A Question Recognition Strategy for Small Data Sets in Medical Insurance QA Systems","publication_year":2024,"publication_date":"2024-04-26","ids":{"openalex":"https://openalex.org/W4402712972","doi":"https://doi.org/10.1145/3653644.3665180"},"language":"en","primary_location":{"id":"doi:10.1145/3653644.3665180","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3653644.3665180","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 3rd International Conference on Frontiers of Artificial Intelligence and Machine Learning","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/A5103688511","display_name":"Shun Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210116247","display_name":"Jingchu University of Technology","ror":"https://ror.org/037kvhq82","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210116247"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shun Zhang","raw_affiliation_strings":["Jingchu University of Technology, China"],"affiliations":[{"raw_affiliation_string":"Jingchu University of Technology, China","institution_ids":["https://openalex.org/I4210116247"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111335760","display_name":"Weiwei Fu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210126577","display_name":"Shanghai Public Security Bureau","ror":"https://ror.org/03cfv5b91","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210126577"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weiwei Fu","raw_affiliation_strings":["Jingmen Duodao District Medical Security Bureau, China"],"affiliations":[{"raw_affiliation_string":"Jingmen Duodao District Medical Security Bureau, China","institution_ids":["https://openalex.org/I4210126577"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113394735","display_name":"Yurong Hu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210116247","display_name":"Jingchu University of Technology","ror":"https://ror.org/037kvhq82","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210116247"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yurong Hu","raw_affiliation_strings":["Jingchu University of Technology;Jingmen Cryptometry Application Technology Research Center, China"],"affiliations":[{"raw_affiliation_string":"Jingchu University of Technology;Jingmen Cryptometry Application Technology Research Center, China","institution_ids":["https://openalex.org/I4210116247"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071838071","display_name":"Huafeng Chen","orcid":"https://orcid.org/0000-0002-2816-1020"},"institutions":[{"id":"https://openalex.org/I4210116247","display_name":"Jingchu University of Technology","ror":"https://ror.org/037kvhq82","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210116247"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huafeng Chen","raw_affiliation_strings":["Jingchu University of Technology, China"],"affiliations":[{"raw_affiliation_string":"Jingchu University of Technology, China","institution_ids":["https://openalex.org/I4210116247"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5103688511"],"corresponding_institution_ids":["https://openalex.org/I4210116247"],"apc_list":null,"apc_paid":null,"fwci":0.8592,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.79800115,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"318","last_page":"324"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14484","display_name":"Technology and Data Analysis","score":0.9878000020980835,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T14484","display_name":"Technology and Data Analysis","score":0.9878000020980835,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9557999968528748,"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":0.9476000070571899,"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/computer-science","display_name":"Computer science","score":0.6745989918708801},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.42343848943710327},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.34533464908599854}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6745989918708801},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.42343848943710327},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34533464908599854}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3653644.3665180","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3653644.3665180","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 3rd International Conference on Frontiers of Artificial Intelligence and Machine Learning","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Decent work and economic growth","score":0.5,"id":"https://metadata.un.org/sdg/8"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1577729528","https://openalex.org/W1981276685","https://openalex.org/W2429488962","https://openalex.org/W2470394683","https://openalex.org/W2508865106","https://openalex.org/W2606882704","https://openalex.org/W2970641574","https://openalex.org/W3101798106","https://openalex.org/W3127402166","https://openalex.org/W3147653261","https://openalex.org/W6600271681","https://openalex.org/W6634628751","https://openalex.org/W6702248584"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W2382290278","https://openalex.org/W4395014643"],"abstract_inverted_index":{"Question":[0],"recognition":[1,9,33],"is":[2,97],"crucial":[3],"in":[4,21,163,172],"Q&A":[5,175],"systems.":[6],"An":[7],"effective":[8],"method":[10,157],"significantly":[11],"enhances":[12],"the":[13,29,131,164,182],"accuracy":[14],"and":[15,42,88,110,120,126,148,168],"efficiency":[16],"of":[17,31,63],"information":[18,87],"retrieval.":[19],"Advances":[20],"natural":[22,78],"language":[23,79],"processing":[24],"(NLP)":[25],"technology":[26],"have":[27],"spurred":[28],"development":[30],"statement":[32],"techniques.":[34],"However,":[35],"many":[36],"methods":[37],"rely":[38],"on":[39],"extensive":[40],"datasets,":[41],"numerous":[43],"sectors":[44],"struggle":[45],"to":[46,53,140,152,181],"keep":[47],"pace":[48],"with":[49,72],"intelligent":[50],"advancements":[51],"due":[52],"data":[54,95,114],"scarcity.":[55],"Policy-related":[56],"questions,":[57],"which":[58],"often":[59],"involve":[60],"a":[61],"plethora":[62],"technical":[64],"terms,":[65],"exemplify":[66],"this":[67,103],"challenge.":[68],"For":[69],"colloquial":[70],"questions":[71,112],"intricate":[73],"structures,":[74],"models":[75,83,162],"require":[76],"enhanced":[77],"understanding":[80],"capabilities.":[81],"These":[82],"must":[84],"prioritize":[85],"key":[86],"filter":[89],"out":[90],"irrelevant":[91],"data,":[92],"particularly":[93],"when":[94],"availability":[96],"limited.":[98],"To":[99],"address":[100],"these":[101],"issues,":[102],"study":[104],"utilized":[105],"medical":[106,142,165],"insurance":[107,143,166],"approval":[108],"items":[109],"related":[111],"as":[113,123],"sources.":[115],"It":[116],"employed":[117],"sentence":[118],"vectors":[119],"question":[121,149,154],"structures":[122],"analytical":[124],"frameworks":[125],"explored":[127],"innovative":[128],"solutions.":[129],"Specifically,":[130],"twin":[132],"network":[133],"fine-tuning":[134],"pre-training":[135],"model":[136],"RoBERTa":[137],"was":[138],"applied":[139],"classify":[141],"items,":[144],"integrating":[145],"syntactic":[146],"structure":[147],"word":[150],"features":[151],"pinpoint":[153],"targets.":[155],"This":[156],"outperformed":[158],"standard":[159],"text":[160],"classification":[161],"domain":[167],"has":[169],"potential":[170],"applicability":[171],"other":[173],"policy-oriented":[174],"systems,":[176],"thereby":[177],"offering":[178],"precise":[179],"services":[180],"public.":[183]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-12-23T23:11:35.936235","created_date":"2025-10-10T00:00:00"}