{"id":"https://openalex.org/W4391096023","doi":"https://doi.org/10.1109/bigdata59044.2023.10386635","title":"Uncertainty Estimation for a Dual-Embedding based Entity Extraction Service","display_name":"Uncertainty Estimation for a Dual-Embedding based Entity Extraction Service","publication_year":2023,"publication_date":"2023-12-15","ids":{"openalex":"https://openalex.org/W4391096023","doi":"https://doi.org/10.1109/bigdata59044.2023.10386635"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata59044.2023.10386635","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bigdata59044.2023.10386635","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5086878357","display_name":"Hamed Khorasgani","orcid":"https://orcid.org/0000-0002-0892-6276"},"institutions":[{"id":"https://openalex.org/I4210132246","display_name":"Workday (United States)","ror":"https://ror.org/02nn1vm89","country_code":"US","type":"company","lineage":["https://openalex.org/I4210132246"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hamed Khorasgani","raw_affiliation_strings":["Workday,Pleasanton,CA,USA","Workday, Pleasanton, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Workday,Pleasanton,CA,USA","institution_ids":["https://openalex.org/I4210132246"]},{"raw_affiliation_string":"Workday, Pleasanton, CA, USA","institution_ids":["https://openalex.org/I4210132246"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093760773","display_name":"Namrata Ghadi","orcid":null},"institutions":[{"id":"https://openalex.org/I4210132246","display_name":"Workday (United States)","ror":"https://ror.org/02nn1vm89","country_code":"US","type":"company","lineage":["https://openalex.org/I4210132246"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Namrata Ghadi","raw_affiliation_strings":["Workday,Pleasanton,CA,USA","Workday, Pleasanton, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Workday,Pleasanton,CA,USA","institution_ids":["https://openalex.org/I4210132246"]},{"raw_affiliation_string":"Workday, Pleasanton, CA, USA","institution_ids":["https://openalex.org/I4210132246"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086979371","display_name":"Saumil Shah","orcid":"https://orcid.org/0000-0003-1642-4687"},"institutions":[{"id":"https://openalex.org/I4210132246","display_name":"Workday (United States)","ror":"https://ror.org/02nn1vm89","country_code":"US","type":"company","lineage":["https://openalex.org/I4210132246"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Saumil Shah","raw_affiliation_strings":["Workday,Pleasanton,CA,USA","Workday, Pleasanton, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Workday,Pleasanton,CA,USA","institution_ids":["https://openalex.org/I4210132246"]},{"raw_affiliation_string":"Workday, Pleasanton, CA, USA","institution_ids":["https://openalex.org/I4210132246"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014777305","display_name":"Henry Zhang","orcid":"https://orcid.org/0000-0003-3744-736X"},"institutions":[{"id":"https://openalex.org/I4210132246","display_name":"Workday (United States)","ror":"https://ror.org/02nn1vm89","country_code":"US","type":"company","lineage":["https://openalex.org/I4210132246"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Henry Zhang","raw_affiliation_strings":["Workday,Pleasanton,CA,USA","Workday, Pleasanton, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Workday,Pleasanton,CA,USA","institution_ids":["https://openalex.org/I4210132246"]},{"raw_affiliation_string":"Workday, Pleasanton, CA, USA","institution_ids":["https://openalex.org/I4210132246"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210132246"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.34430165,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"33","issue":null,"first_page":"1940","last_page":"1944"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11719","display_name":"Data Quality and Management","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11719","display_name":"Data Quality and Management","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9975000023841858,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.994700014591217,"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":0.7553727626800537},{"id":"https://openalex.org/keywords/dual","display_name":"Dual (grammatical number)","score":0.6991479396820068},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.6932732462882996},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6873566508293152},{"id":"https://openalex.org/keywords/measurement-uncertainty","display_name":"Measurement uncertainty","score":0.6068470478057861},{"id":"https://openalex.org/keywords/uncertainty-analysis","display_name":"Uncertainty analysis","score":0.4901653230190277},{"id":"https://openalex.org/keywords/service","display_name":"Service (business)","score":0.47110751271247864},{"id":"https://openalex.org/keywords/uncertainty-quantification","display_name":"Uncertainty quantification","score":0.46921101212501526},{"id":"https://openalex.org/keywords/extraction","display_name":"Extraction (chemistry)","score":0.44806963205337524},{"id":"https://openalex.org/keywords/production","display_name":"Production (economics)","score":0.4416433870792389},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.38430339097976685},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3120075464248657},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.24426808953285217},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1088959276676178},{"id":"https://openalex.org/keywords/systems-engineering","display_name":"Systems engineering","score":0.10693886876106262},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10681778192520142},{"id":"https://openalex.org/keywords/simulation","display_name":"Simulation","score":0.09378626942634583},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.08010146021842957}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7553727626800537},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.6991479396820068},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.6932732462882996},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6873566508293152},{"id":"https://openalex.org/C137209882","wikidata":"https://www.wikidata.org/wiki/Q1403517","display_name":"Measurement uncertainty","level":2,"score":0.6068470478057861},{"id":"https://openalex.org/C177803969","wikidata":"https://www.wikidata.org/wiki/Q29205","display_name":"Uncertainty analysis","level":2,"score":0.4901653230190277},{"id":"https://openalex.org/C2780378061","wikidata":"https://www.wikidata.org/wiki/Q25351891","display_name":"Service (business)","level":2,"score":0.47110751271247864},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.46921101212501526},{"id":"https://openalex.org/C4725764","wikidata":"https://www.wikidata.org/wiki/Q844704","display_name":"Extraction (chemistry)","level":2,"score":0.44806963205337524},{"id":"https://openalex.org/C2778348673","wikidata":"https://www.wikidata.org/wiki/Q739302","display_name":"Production (economics)","level":2,"score":0.4416433870792389},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38430339097976685},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3120075464248657},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.24426808953285217},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1088959276676178},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.10693886876106262},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10681778192520142},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.09378626942634583},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.08010146021842957},{"id":"https://openalex.org/C139719470","wikidata":"https://www.wikidata.org/wiki/Q39680","display_name":"Macroeconomics","level":1,"score":0.0},{"id":"https://openalex.org/C136264566","wikidata":"https://www.wikidata.org/wiki/Q159810","display_name":"Economy","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C124952713","wikidata":"https://www.wikidata.org/wiki/Q8242","display_name":"Literature","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"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/bigdata59044.2023.10386635","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bigdata59044.2023.10386635","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1995806857","https://openalex.org/W2091230023","https://openalex.org/W2295598076","https://openalex.org/W2896457183","https://openalex.org/W2951696358","https://openalex.org/W2954996726","https://openalex.org/W3105472275","https://openalex.org/W3176923149","https://openalex.org/W3183048323","https://openalex.org/W6737496325","https://openalex.org/W6754205108","https://openalex.org/W6755207826","https://openalex.org/W6777766138","https://openalex.org/W6785878756","https://openalex.org/W6794739717"],"related_works":["https://openalex.org/W93577605","https://openalex.org/W3212153563","https://openalex.org/W3130844878","https://openalex.org/W4312671192","https://openalex.org/W2387053421","https://openalex.org/W2057769593","https://openalex.org/W4249135812","https://openalex.org/W4205181462","https://openalex.org/W3139964550","https://openalex.org/W2376953431"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3,18,48],"present":[4,27,49,63],"our":[5,11,28,45,50],"approach":[6,53],"for":[7],"quantifying":[8],"uncertainty":[9,22,41,51,75,92],"in":[10,57,78],"dual-embedding":[12],"based":[13],"entity":[14,29],"extraction":[15,30],"system.":[16,46],"First,":[17],"review":[19],"the":[20,40,64,79,85],"state-of-the-art":[21],"estimation":[23,42,76,93],"methods.":[24],"We":[25],"then":[26],"system":[31],"and":[32,54,66],"discuss":[33],"practical":[34,91],"challenges":[35,65],"of":[36,39],"applying":[37],"each":[38],"solutions":[43],"to":[44,62,88],"Finally,":[47],"estimate":[52],"its":[55],"performance":[56],"production.":[58],"Our":[59],"goal":[60],"is":[61],"concerns":[67],"that":[68],"one":[69],"should":[70],"consider":[71],"while":[72],"designing":[73],"an":[74],"solution":[77],"real":[80],"world":[81],"and,":[82],"therefore,":[83],"help":[84],"research":[86],"community":[87],"develop":[89],"more":[90],"solutions.":[94]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
