{"id":"https://openalex.org/W4402030149","doi":"https://doi.org/10.1145/3669754.3669801","title":"Research on Entity Representation Bias Adjustment in Causal Relationship Extraction","display_name":"Research on Entity Representation Bias Adjustment in Causal Relationship Extraction","publication_year":2024,"publication_date":"2024-04-26","ids":{"openalex":"https://openalex.org/W4402030149","doi":"https://doi.org/10.1145/3669754.3669801"},"language":"en","primary_location":{"id":"doi:10.1145/3669754.3669801","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3669754.3669801","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 10th International Conference on Computing and Artificial Intelligence","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/A5079824167","display_name":"Jiaoyun Yang","orcid":"https://orcid.org/0000-0002-0233-590X"},"institutions":[{"id":"https://openalex.org/I16365422","display_name":"Hefei University of Technology","ror":"https://ror.org/02czkny70","country_code":"CN","type":"education","lineage":["https://openalex.org/I16365422"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jiaoyun Yang","raw_affiliation_strings":["Hefei University of Technology, China"],"affiliations":[{"raw_affiliation_string":"Hefei University of Technology, China","institution_ids":["https://openalex.org/I16365422"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5106839076","display_name":"Dongyan Ji","orcid":null},"institutions":[{"id":"https://openalex.org/I16365422","display_name":"Hefei University of Technology","ror":"https://ror.org/02czkny70","country_code":"CN","type":"education","lineage":["https://openalex.org/I16365422"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dongyan Ji","raw_affiliation_strings":["Hefei University of Technology, China"],"affiliations":[{"raw_affiliation_string":"Hefei University of Technology, China","institution_ids":["https://openalex.org/I16365422"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110379203","display_name":"Mei Long","orcid":null},"institutions":[{"id":"https://openalex.org/I4210088165","display_name":"Anhui Sanlian University","ror":"https://ror.org/012sfmg27","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210088165"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Long Mei","raw_affiliation_strings":["Anhui Sanlian University, China"],"affiliations":[{"raw_affiliation_string":"Anhui Sanlian University, China","institution_ids":["https://openalex.org/I4210088165"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5091586702","display_name":"Min Hu","orcid":"https://orcid.org/0000-0003-2122-0240"},"institutions":[{"id":"https://openalex.org/I16365422","display_name":"Hefei University of Technology","ror":"https://ror.org/02czkny70","country_code":"CN","type":"education","lineage":["https://openalex.org/I16365422"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Min Hu","raw_affiliation_strings":["Hefei University of Technology, China"],"affiliations":[{"raw_affiliation_string":"Hefei University of Technology, China","institution_ids":["https://openalex.org/I16365422"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5079824167"],"corresponding_institution_ids":["https://openalex.org/I16365422"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.12014413,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"307","last_page":"312"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9970999956130981,"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":0.9970999956130981,"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/T11719","display_name":"Data Quality and Management","score":0.9950000047683716,"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/T12016","display_name":"Web Data Mining and Analysis","score":0.9940000176429749,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6744199991226196},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.6097930669784546},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.487753689289093},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4784139096736908}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6744199991226196},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.6097930669784546},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.487753689289093},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4784139096736908},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3669754.3669801","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3669754.3669801","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 10th International Conference on Computing and Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5899999737739563,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1991018417","https://openalex.org/W2145870355","https://openalex.org/W2608420543","https://openalex.org/W2759211898","https://openalex.org/W2773078063","https://openalex.org/W2791170418","https://openalex.org/W2885051956","https://openalex.org/W2887428522","https://openalex.org/W2952929029","https://openalex.org/W2963351448","https://openalex.org/W2984452801","https://openalex.org/W3102116353","https://openalex.org/W3102895271","https://openalex.org/W3176001432","https://openalex.org/W3181414820","https://openalex.org/W3194836374","https://openalex.org/W3209419357","https://openalex.org/W4224316507","https://openalex.org/W6675557394"],"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/W3204019825"],"abstract_inverted_index":{"Causal":[0],"relationship":[1,71],"extraction":[2],"is":[3,104,121,130,161],"a":[4,80,93,116,125],"crucial":[5],"task":[6],"in":[7,50,64,101,136,197,201],"the":[8,15,45,51,83,87,151,164,171,177,186,198,211,220,227,235],"field":[9],"of":[10,17,48,86,153,207,223,238],"natural":[11],"language":[12],"processing,":[13],"involving":[14],"determination":[16],"causal":[18,36,40,70],"relationships":[19],"between":[20,57],"entities":[21,58],"or":[22],"events":[23],"based":[24,73],"on":[25,74,181,229],"textual":[26],"descriptions.":[27],"It":[28],"can":[29,192],"be":[30],"applied":[31,122,162],"to":[32,44,62,82,95,123,132,163,166,176,215],"subsequent":[33],"tasks":[34],"like":[35],"graph":[37],"construction":[38],"and":[39,59,114],"inference.":[41],"However,":[42],"due":[43],"imbalanced":[46],"distribution":[47],"labels":[49],"dataset,":[52,199],"spurious":[53],"associations":[54],"may":[55,68],"arise":[56],"labels,":[60],"leading":[61],"biases":[63,168],"entity":[65,77,97,102,107,137,157,187,194],"representations.":[66],"Models":[67],"make":[69],"judgments":[72],"these":[75],"biased":[76],"representations,":[78,113],"posing":[79],"challenge":[81],"generalization":[84],"ability":[85],"models.":[88],"To":[89,149],"address":[90],"this":[91,239],"issue,":[92],"strategy":[94,191],"mitigate":[96],"bias":[98,189,196],"through":[99],"intervention":[100],"representations":[103,108,138],"proposed.":[105],"First,":[106],"are":[109],"extracted":[110],"from":[111,170,174],"sentence":[112],"then":[115],"Principal":[117],"Component":[118],"Analysis":[119],"method":[120],"obtain":[124],"semantic":[126,134],"subspace.":[127],"This":[128],"subspace":[129],"used":[131],"eliminate":[133],"information":[135],"that":[139,185],"act":[140],"as":[141],"confounding":[142],"factors":[143],"while":[144],"retaining":[145],"more":[146],"contextual":[147],"information.":[148],"ensure":[150],"fairness":[152],"model":[154],"evaluation,":[155],"an":[156,202],"pair":[158],"non-overlapping":[159],"division":[160],"dataset":[165],"prevent":[167],"learned":[169],"training":[172],"set":[173],"transferring":[175],"test":[178],"set.":[179],"Experiments":[180],"three":[182,230],"datasets":[183],"demonstrate":[184,234],"representation":[188,195],"adjustment":[190],"alleviate":[193],"resulting":[200],"average":[203],"F1":[204],"score":[205],"improvement":[206,222],"approximately":[208,224],"1.44%":[209],"over":[210],"baseline":[212,232],"model.":[213],"Compared":[214],"other":[216],"methods,":[217],"it":[218],"achieves":[219],"highest":[221],"5.93%.":[225],"Moreover,":[226],"results":[228],"different":[231],"models":[233],"model-agnostic":[236],"nature":[237],"approach.":[240]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}