{"id":"https://openalex.org/W3007318604","doi":"https://doi.org/10.1109/bigdata47090.2019.9006248","title":"Empirical Comparisons of CNN with Other Learning Algorithms for Text Classification in Legal Document Review","display_name":"Empirical Comparisons of CNN with Other Learning Algorithms for Text Classification in Legal Document Review","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W3007318604","doi":"https://doi.org/10.1109/bigdata47090.2019.9006248","mag":"3007318604"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata47090.2019.9006248","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9006248","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data (Big Data)","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/A5035561684","display_name":"Robert Keeling","orcid":null},"institutions":[{"id":"https://openalex.org/I861268105","display_name":"Sidley Austin","ror":"https://ror.org/05a8r2n20","country_code":"US","type":"other","lineage":["https://openalex.org/I861268105"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Robert Keeling","raw_affiliation_strings":["Esq. Complex Commercial Litigation Sidley Austin LLP, Washington, DC, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Esq. Complex Commercial Litigation Sidley Austin LLP, Washington, DC, USA","institution_ids":["https://openalex.org/I861268105"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069210667","display_name":"Rishi Chhatwal","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rishi Chhatwal","raw_affiliation_strings":["Esq. Legal AT&T Services, Inc., Washington, DC, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Esq. Legal AT&T Services, Inc., Washington, DC, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049186333","display_name":"Nathaniel Huber-Fliflet","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nathaniel Huber-Fliflet","raw_affiliation_strings":["Data & Technology Ankura, Washington, DC, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Data & Technology Ankura, Washington, DC, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110061553","display_name":"Jianping Zhang","orcid":"https://orcid.org/0000-0003-0815-009X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jianping Zhang","raw_affiliation_strings":["Data & Technology Ankura, Washington, DC, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Data & Technology Ankura, Washington, DC, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052463733","display_name":"Fusheng Wei","orcid":"https://orcid.org/0009-0000-5881-5307"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fusheng Wei","raw_affiliation_strings":["Data & Technology Ankura, Washington, DC, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Data & Technology Ankura, Washington, DC, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112689759","display_name":"Haozhen Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Haozhen Zhao","raw_affiliation_strings":["Data & Technology Ankura, Washington, DC, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Data & Technology Ankura, Washington, DC, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032717078","display_name":"Shi Ye","orcid":"https://orcid.org/0000-0002-1354-0650"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ye Shi","raw_affiliation_strings":["Data & Technology Ankura, Washington, DC, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Data & Technology Ankura, Washington, DC, USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101414674","display_name":"Han Qin","orcid":"https://orcid.org/0000-0003-3786-3910"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Han Qin","raw_affiliation_strings":["Data & Technology Ankura, Washington, DC, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Data & Technology Ankura, Washington, DC, USA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":23.3504,"has_fulltext":false,"cited_by_count":25,"citation_normalized_percentile":{"value":0.9940706,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"2038","last_page":"2042"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13643","display_name":"Artificial Intelligence in Law","score":0.9783999919891357,"subfield":{"id":"https://openalex.org/subfields/3320","display_name":"Political Science and International Relations"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T13643","display_name":"Artificial Intelligence in Law","score":0.9783999919891357,"subfield":{"id":"https://openalex.org/subfields/3320","display_name":"Political Science and International Relations"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T13910","display_name":"Computational and Text Analysis Methods","score":0.9746000170707703,"subfield":{"id":"https://openalex.org/subfields/3300","display_name":"General Social Sciences"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9739000201225281,"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":0.7818419933319092},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.7036148905754089},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.695763111114502},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5909823179244995},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5432769656181335},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.526098370552063},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4624486267566681},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.46107912063598633},{"id":"https://openalex.org/keywords/statistical-classification","display_name":"Statistical classification","score":0.4549242854118347},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.42960628867149353},{"id":"https://openalex.org/keywords/f1-score","display_name":"F1 score","score":0.42883360385894775},{"id":"https://openalex.org/keywords/coding","display_name":"Coding (social sciences)","score":0.4278295040130615},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4261695444583893},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11068162322044373}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7818419933319092},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7036148905754089},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.695763111114502},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5909823179244995},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5432769656181335},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.526098370552063},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4624486267566681},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.46107912063598633},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.4549242854118347},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.42960628867149353},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.42883360385894775},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.4278295040130615},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4261695444583893},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11068162322044373},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata47090.2019.9006248","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9006248","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W1888026739","https://openalex.org/W2413512986","https://openalex.org/W2413904250","https://openalex.org/W2511794618","https://openalex.org/W2740721704","https://openalex.org/W2912267070","https://openalex.org/W2949541494","https://openalex.org/W2963083845","https://openalex.org/W2963921497"],"related_works":["https://openalex.org/W2034680797","https://openalex.org/W4207040723","https://openalex.org/W2967426019","https://openalex.org/W2990537558","https://openalex.org/W3210752578","https://openalex.org/W2621610018","https://openalex.org/W4372055028","https://openalex.org/W2969401632","https://openalex.org/W2010770252","https://openalex.org/W4390481936"],"abstract_inverted_index":{"Research":[0],"has":[1,28],"shown":[2],"that":[3,37,126,131,137,166],"Convolutional":[4],"Neural":[5],"Networks":[6],"(CNN)":[7],"can":[8],"be":[9],"effectively":[10],"applied":[11],"to":[12,26],"text":[13,71],"classification":[14,86],"as":[15],"part":[16],"of":[17,43,58,111,144,162],"a":[18,105],"predictive":[19],"coding":[20],"protocol.":[21],"That":[22],"said,":[23],"most":[24],"research":[25,156],"date":[27],"been":[29],"conducted":[30],"on":[31],"data":[32,51,84,145],"sets":[33,146],"with":[34,56,64,90],"short":[35],"documents":[36,44,57],"do":[38],"not":[39],"reflect":[40],"the":[41,114,139,142,158],"variety":[42],"in":[45],"real":[46],"world":[47],"document":[48],"reviews.":[49],"Using":[50],"from":[52],"four":[53],"actual":[54],"reviews":[55],"varying":[59],"lengths,":[60],"we":[61],"compared":[62,117],"CNN":[63,127],"other":[65],"popular":[66],"machine":[67,163],"learning":[68,97,164],"algorithms":[69,165],"for":[70],"classification,":[72],"including":[73],"Logistic":[74],"Regression,":[75],"Support":[76],"Vector":[77],"Machine,":[78],"and":[79,113,120,147],"Random":[80],"Forest.":[81],"For":[82],"each":[83],"set,":[85],"models":[87,100],"were":[88,101,116],"trained":[89],"different":[91,96],"training":[92,148],"sample":[93,149],"sizes":[94],"using":[95,104,118],"algorithms.":[98],"These":[99,151],"then":[102],"evaluated":[103],"large":[106],"randomly":[107],"sampled":[108],"test":[109],"set":[110],"documents,":[112],"results":[115,152],"precision":[119],"recall":[121],"curves.":[122],"Our":[123],"study":[124],"demonstrates":[125],"performed":[128,138],"well,":[129],"but":[130],"there":[132],"was":[133],"no":[134],"single":[135],"algorithm":[136],"best":[140],"across":[141],"combination":[143],"sizes.":[150],"will":[153],"help":[154],"advance":[155],"into":[157],"legal":[159],"profession's":[160],"use":[161],"maximize":[167],"performance.":[168]},"counts_by_year":[{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}