{"id":"https://openalex.org/W3121122840","doi":null,"title":"Measuring the Effects of Bias in Training Data for Literary Classification","display_name":"Measuring the Effects of Bias in Training Data for Literary Classification","publication_year":2020,"publication_date":"2020-12-01","ids":{"openalex":"https://openalex.org/W3121122840","mag":"3121122840"},"language":"en","primary_location":{"id":"mag:3121122840","doi":null,"is_oa":false,"landing_page_url":"https://www.aclweb.org/anthology/2020.latechclfl-1.9/","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"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/A5037948197","display_name":"Sunyam Bagga","orcid":"https://orcid.org/0000-0001-6994-2192","observed_orcids":["https://orcid.org/0000-0001-6994-2192"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sunyam Bagga","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0001-6994-2192","affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5027417114","display_name":"Andrew Piper","orcid":"https://orcid.org/0000-0001-9663-5999","observed_orcids":["https://orcid.org/0000-0001-9663-5999"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Andrew Piper","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0001-9663-5999","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0837,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.39968171,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":93},"biblio":{"volume":null,"issue":null,"first_page":"74","last_page":"84"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10883","display_name":"Ethics and Social Impacts of AI","score":0.38109999895095825,"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/T10883","display_name":"Ethics and Social Impacts of AI","score":0.38109999895095825,"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/T12380","display_name":"Authorship Attribution and Profiling","score":0.2433999925851822,"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/T10028","display_name":"Topic Modeling","score":0.08829999715089798,"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/training-data-bias","display_name":"training data bias","score":0.8809999823570251},{"id":"https://openalex.org/keywords/literary-classification","display_name":"literary classification","score":0.9570000171661377},{"id":"https://openalex.org/keywords/social-bias","display_name":"social bias","score":0.9959999918937683},{"id":"https://openalex.org/keywords/bert","display_name":"BERT","score":0.7919999957084656},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"support vector machine","score":0.9359999895095825},{"id":"https://openalex.org/keywords/bias-robustness","display_name":"bias robustness","score":0.6669999957084656}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5645922422409058},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.5008575916290283},{"id":"https://openalex.org/C60671577","wikidata":"https://www.wikidata.org/wiki/Q210272","display_name":"Cultural heritage","level":2,"score":0.48556777834892273},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44732189178466797},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44259345531463623},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4381411075592041},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4368010461330414},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.4306703805923462},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.4168654978275299},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.40783703327178955},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3843018114566803},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.1367942988872528},{"id":"https://openalex.org/C95457728","wikidata":"https://www.wikidata.org/wiki/Q309","display_name":"History","level":0,"score":0.09324750304222107},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","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}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:3121122840","doi":null,"is_oa":false,"landing_page_url":"https://www.aclweb.org/anthology/2020.latechclfl-1.9/","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.5676000118255615,"id":"https://metadata.un.org/sdg/10"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W68178626","https://openalex.org/W1522301498","https://openalex.org/W1979651826","https://openalex.org/W2031253971","https://openalex.org/W2032536435","https://openalex.org/W2044222399","https://openalex.org/W2095881835","https://openalex.org/W2096765155","https://openalex.org/W2161098141","https://openalex.org/W2163605009","https://openalex.org/W2250539671","https://openalex.org/W2483215953","https://openalex.org/W2500585446","https://openalex.org/W2786709099","https://openalex.org/W2787560479","https://openalex.org/W2793978524","https://openalex.org/W2794819672","https://openalex.org/W2796916255","https://openalex.org/W2893425640","https://openalex.org/W2913116882","https://openalex.org/W2943032471","https://openalex.org/W2949678053","https://openalex.org/W2950813464","https://openalex.org/W2963341956","https://openalex.org/W2970971581","https://openalex.org/W2971202292","https://openalex.org/W2971296908","https://openalex.org/W2972972637","https://openalex.org/W2980282514","https://openalex.org/W3005206189","https://openalex.org/W3032388710","https://openalex.org/W3128232076","https://openalex.org/W3156333129"],"related_works":["https://openalex.org/W7164007520","https://openalex.org/W6977344210","https://openalex.org/W7168049878","https://openalex.org/W4384831526","https://openalex.org/W6892742806","https://openalex.org/W2791170418","https://openalex.org/W3018197773","https://openalex.org/W4386804534","https://openalex.org/W2968044868","https://openalex.org/W4376643691"],"abstract_inverted_index":{"Downstream":[0],"effects":[1,44,135,189],"of":[2,11,24,45,48,59,66,80,91,110,136,139,144,165,190,203],"biased":[3],"training":[4,54,111,145,193],"data":[5,55,112,194],"have":[6],"become":[7],"a":[8],"major":[9],"concern":[10],"the":[12,19,43,57,88,134,162,173,187,201],"NLP":[13],"community.":[14],"How":[15],"this":[16,34,123,180],"may":[17,131],"impact":[18],"automated":[20],"curation":[21],"and":[22,50,95],"annotation":[23],"cultural":[25,67,117,197],"heritage":[26,118,198],"material":[27,199],"is":[28,82,103,125],"currently":[29],"not":[30],"well":[31],"known.":[32],"In":[33],"work,":[35],"we":[36],"create":[37],"an":[38,107],"experimental":[39],"framework":[40],"to":[41,105,116,126],"measure":[42],"different":[46,92,137,163],"types":[47,94,138],"stylistic":[49],"social":[51],"bias":[52,102,140,166,191],"within":[53,141,167,192],"for":[56,195],"purposes":[58],"literary":[60],"classification,":[61],"as":[62,154],"one":[63],"important":[64],"subclass":[65],"material.":[68,119],"Because":[69],"historical":[70],"collections":[71],"are":[72,158],"often":[73,85],"sparsely":[74],"annotated,":[75],"much":[76],"like":[77],"our":[78,168],"knowledge":[79],"history":[81],"incomplete,":[83],"researchers":[84],"cannot":[86],"know":[87],"underlying":[89],"distributions":[90],"document":[93],"their":[96],"various":[97],"sub-classes.":[98],"This":[99],"means":[100],"that":[101,149,179],"likely":[104],"be":[106],"intrinsic":[108],"feature":[109],"when":[113],"it":[114],"comes":[115],"Our":[120],"aim":[121],"in":[122,172],"study":[124,202],"investigate":[127],"which":[128],"classification":[129],"methods":[130],"help":[132],"mitigate":[133],"curated":[142],"samples":[143],"data.":[146],"We":[147,177],"find":[148],"machine":[150],"learning":[151],"techniques":[152],"such":[153],"BERT":[155],"or":[156],"SVM":[157],"robust":[159],"against":[160],"reproducing":[161],"kinds":[164],"test":[169],"data,":[170],"except":[171],"most":[174],"extreme":[175],"cases.":[176],"hope":[178],"work":[181],"will":[182],"spur":[183],"further":[184],"research":[185],"into":[186],"potential":[188],"other":[196],"beyond":[200],"literature.":[204]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2021-01-18T00:00:00"}