{"id":"https://openalex.org/W4405634121","doi":"https://doi.org/10.1109/mlnlp63328.2024.10800300","title":"MLLMIE: Multiple Large Language Model Information Extraction in Ancient Medical Texts","display_name":"MLLMIE: Multiple Large Language Model Information Extraction in Ancient Medical Texts","publication_year":2024,"publication_date":"2024-10-18","ids":{"openalex":"https://openalex.org/W4405634121","doi":"https://doi.org/10.1109/mlnlp63328.2024.10800300"},"language":"en","primary_location":{"id":"doi:10.1109/mlnlp63328.2024.10800300","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlnlp63328.2024.10800300","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 7th International Conference on Machine Learning and Natural Language Processing (MLNLP)","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/A5101844321","display_name":"Feilong Wang","orcid":"https://orcid.org/0000-0003-2696-5620"},"institutions":[{"id":"https://openalex.org/I118987531","display_name":"Anhui Jianzhu University","ror":"https://ror.org/0108wjw08","country_code":"CN","type":"education","lineage":["https://openalex.org/I118987531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feilong Wang","raw_affiliation_strings":["School of Electronics and Information Engineering, Anhui Jianzhu University Mass Spectrometry Key Technology R&#x0026;D and Clinical Application of Anhui Province Jointly Constructed Discipline Key Experiments,Hefei,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronics and Information Engineering, Anhui Jianzhu University Mass Spectrometry Key Technology R&#x0026;D and Clinical Application of Anhui Province Jointly Constructed Discipline Key Experiments,Hefei,China","institution_ids":["https://openalex.org/I118987531"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034906558","display_name":"Donghui Shi","orcid":"https://orcid.org/0000-0001-5301-368X"},"institutions":[{"id":"https://openalex.org/I118987531","display_name":"Anhui Jianzhu University","ror":"https://ror.org/0108wjw08","country_code":"CN","type":"education","lineage":["https://openalex.org/I118987531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Donghui Shi","raw_affiliation_strings":["School of Electronics and Information Engineering, Anhui Jianzhu University Mass Spectrometry Key Technology R&#x0026;D and Clinical Application of Anhui Province Jointly Constructed Discipline Key Experiments,Hefei,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronics and Information Engineering, Anhui Jianzhu University Mass Spectrometry Key Technology R&#x0026;D and Clinical Application of Anhui Province Jointly Constructed Discipline Key Experiments,Hefei,China","institution_ids":["https://openalex.org/I118987531"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038890073","display_name":"Xinyi Cui","orcid":"https://orcid.org/0000-0003-1411-9558"},"institutions":[{"id":"https://openalex.org/I118987531","display_name":"Anhui Jianzhu University","ror":"https://ror.org/0108wjw08","country_code":"CN","type":"education","lineage":["https://openalex.org/I118987531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinyi Cui","raw_affiliation_strings":["School of Electronics and Information Engineering, Anhui Jianzhu University,Hefei,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronics and Information Engineering, Anhui Jianzhu University,Hefei,China","institution_ids":["https://openalex.org/I118987531"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I118987531"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.20169108,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9686999917030334,"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/T10028","display_name":"Topic Modeling","score":0.9686999917030334,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9667999744415283,"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":0.7159958481788635},{"id":"https://openalex.org/keywords/information-extraction","display_name":"Information extraction","score":0.5720729231834412},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5259982347488403},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.4651881754398346},{"id":"https://openalex.org/keywords/extraction","display_name":"Extraction (chemistry)","score":0.45772773027420044},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.43484750390052795},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.074665367603302}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7159958481788635},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.5720729231834412},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5259982347488403},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.4651881754398346},{"id":"https://openalex.org/C4725764","wikidata":"https://www.wikidata.org/wiki/Q844704","display_name":"Extraction (chemistry)","level":2,"score":0.45772773027420044},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43484750390052795},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.074665367603302},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mlnlp63328.2024.10800300","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlnlp63328.2024.10800300","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 7th International Conference on Machine Learning and Natural Language Processing (MLNLP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.6899999976158142}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W143521272","https://openalex.org/W1549517954","https://openalex.org/W1986398135","https://openalex.org/W2042188227","https://openalex.org/W2053238041","https://openalex.org/W2296283641","https://openalex.org/W2361054264","https://openalex.org/W2517194566","https://openalex.org/W2622740016","https://openalex.org/W2734519248","https://openalex.org/W2914344132","https://openalex.org/W3023186271","https://openalex.org/W3158539798","https://openalex.org/W4387709463","https://openalex.org/W4389611324","https://openalex.org/W4391846077","https://openalex.org/W4392867426","https://openalex.org/W4399676190","https://openalex.org/W4399923931","https://openalex.org/W4400602261","https://openalex.org/W4400739781","https://openalex.org/W6779857854","https://openalex.org/W6850671237"],"related_works":["https://openalex.org/W2377297411","https://openalex.org/W2169518243","https://openalex.org/W3148217948","https://openalex.org/W2375788636","https://openalex.org/W2358561207","https://openalex.org/W2975617233","https://openalex.org/W2388704129","https://openalex.org/W2392827053","https://openalex.org/W3204019825","https://openalex.org/W2368651715"],"abstract_inverted_index":{"Ancient":[0],"medical":[1,17,28,120,141,163,168,181,193],"texts,":[2,164],"which":[3],"reflect":[4],"traditional":[5],"cultural":[6],"practices,":[7],"can":[8],"play":[9],"an":[10],"important":[11],"role":[12],"in":[13,36,161],"the":[14,20,31,52,61,97,104,126,134,139,177,187],"promotion":[15],"of":[16,22,54,58,150,179,191],"traditions.":[18],"Nevertheless,":[19],"complexity":[21],"extracting":[23],"information":[24,41,55,67,113,171,183],"from":[25,69,114],"classical":[26],"Chinese":[27],"texts":[29],"and":[30,34,73,100,156,185,189],"potential":[32],"inconsistencies":[33],"hallucinations":[35],"large":[37,70],"language":[38,71],"models":[39,72],"during":[40],"extraction":[42,56,68,160,184],"present":[43],"significant":[44],"challenges.":[45],"In":[46],"domains":[47],"such":[48],"as":[49,79],"medicine,":[50],"where":[51],"precision":[53],"is":[57],"paramount":[59],"importance,":[60],"paper":[62],"augmented":[63],"existing":[64],"methodologies":[65],"for":[66,152,158],"proposed":[74],"a":[75,91],"novel":[76],"approach,":[77],"designated":[78],"Multiple":[80],"Large":[81],"Language":[82],"Model":[83],"Information":[84],"Extraction":[85],"(MLLMIE).":[86],"The":[87,145],"method":[88,146,174],"initially":[89],"defined":[90],"pattern":[92],"layer":[93,129],"to":[94,111,132],"explicitly":[95],"delineate":[96],"concepts,":[98],"terms,":[99],"their":[101],"interrelationships":[102],"within":[103],"text.":[105],"Subsequently,":[106],"it":[107],"employed":[108,131],"prompt":[109],"engineering":[110],"extract":[112],"multiple":[115],"models,":[116],"thereby":[117,137,165],"obtaining":[118,138],"candidate":[119,135],"ancient":[121,142,162,169,180,192],"text":[122,143,170,182,194],"knowledge":[123],"results.":[124],"Finally,":[125],"model":[127],"validation":[128],"was":[130],"filter":[133],"results,":[136],"final":[140],"knowledge.":[144,195],"attains":[147],"F1":[148],"scores":[149],"92.68%":[151],"named":[153],"entity":[154],"recognition":[155],"94.91%":[157],"relation":[159],"demonstrating":[166],"high-quality":[167],"extraction.":[172],"This":[173],"effectively":[175],"enhances":[176],"accuracy":[178],"facilitates":[186],"discovery":[188],"application":[190]},"counts_by_year":[],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
