{"id":"https://openalex.org/W4391092873","doi":"https://doi.org/10.1109/bigdata59044.2023.10386783","title":"An Empirical Analysis of Text Segmentation for BERT Classification in Extended Documents","display_name":"An Empirical Analysis of Text Segmentation for BERT Classification in Extended Documents","publication_year":2023,"publication_date":"2023-12-15","ids":{"openalex":"https://openalex.org/W4391092873","doi":"https://doi.org/10.1109/bigdata59044.2023.10386783"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata59044.2023.10386783","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata59044.2023.10386783","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/A5053350610","display_name":"Jingchao Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I68939703","display_name":"Fujikura (United States)","ror":"https://ror.org/00qpbjw91","country_code":"US","type":"company","lineage":["https://openalex.org/I4210098230","https://openalex.org/I68939703"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jingchao Yang","raw_affiliation_strings":["Ankura Consulting Group, LLC,Data & Technology,Washington,D.C.,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ankura Consulting Group, LLC,Data & Technology,Washington,D.C.,USA","institution_ids":["https://openalex.org/I68939703"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052463733","display_name":"Fusheng Wei","orcid":"https://orcid.org/0009-0000-5881-5307"},"institutions":[{"id":"https://openalex.org/I68939703","display_name":"Fujikura (United States)","ror":"https://ror.org/00qpbjw91","country_code":"US","type":"company","lineage":["https://openalex.org/I4210098230","https://openalex.org/I68939703"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fusheng Wei","raw_affiliation_strings":["Ankura Consulting Group, LLC,Data & Technology,Washington,D.C.,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ankura Consulting Group, LLC,Data & Technology,Washington,D.C.,USA","institution_ids":["https://openalex.org/I68939703"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049186333","display_name":"Nathaniel Huber-Fliflet","orcid":null},"institutions":[{"id":"https://openalex.org/I68939703","display_name":"Fujikura (United States)","ror":"https://ror.org/00qpbjw91","country_code":"US","type":"company","lineage":["https://openalex.org/I4210098230","https://openalex.org/I68939703"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nathaniel Huber-Fliflet","raw_affiliation_strings":["Ankura Consulting Group, LLC,Data & Technology,Washington,D.C.,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ankura Consulting Group, LLC,Data & Technology,Washington,D.C.,USA","institution_ids":["https://openalex.org/I68939703"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101619455","display_name":"Adam D\u0105browski","orcid":"https://orcid.org/0000-0002-9385-6080"},"institutions":[{"id":"https://openalex.org/I68939703","display_name":"Fujikura (United States)","ror":"https://ror.org/00qpbjw91","country_code":"US","type":"company","lineage":["https://openalex.org/I4210098230","https://openalex.org/I68939703"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Adam Dabrowski","raw_affiliation_strings":["Ankura Consulting Group, LLC,Data & Technology,Washington,D.C.,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ankura Consulting Group, LLC,Data & Technology,Washington,D.C.,USA","institution_ids":["https://openalex.org/I68939703"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101453263","display_name":"Qiang Mao","orcid":"https://orcid.org/0000-0002-9382-6527"},"institutions":[{"id":"https://openalex.org/I68939703","display_name":"Fujikura (United States)","ror":"https://ror.org/00qpbjw91","country_code":"US","type":"company","lineage":["https://openalex.org/I4210098230","https://openalex.org/I68939703"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Qiang Mao","raw_affiliation_strings":["Ankura Consulting Group, LLC,Data & Technology,Washington,D.C.,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ankura Consulting Group, LLC,Data & Technology,Washington,D.C.,USA","institution_ids":["https://openalex.org/I68939703"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101414673","display_name":"Han Qin","orcid":"https://orcid.org/0000-0001-9776-5007"},"institutions":[{"id":"https://openalex.org/I68939703","display_name":"Fujikura (United States)","ror":"https://ror.org/00qpbjw91","country_code":"US","type":"company","lineage":["https://openalex.org/I4210098230","https://openalex.org/I68939703"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Han Qin","raw_affiliation_strings":["Ankura Consulting Group, LLC,Data & Technology,Washington,D.C.,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ankura Consulting Group, LLC,Data & Technology,Washington,D.C.,USA","institution_ids":["https://openalex.org/I68939703"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I68939703"],"apc_list":null,"apc_paid":null,"fwci":0.6534,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.71345855,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"2793","last_page":"2797"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9991000294685364,"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":0.9991000294685364,"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/T12016","display_name":"Web Data Mining and Analysis","score":0.998199999332428,"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"}},{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9937000274658203,"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":0.7521777153015137},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5270220041275024},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.46535947918891907},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.45918482542037964},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4515322148799896}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7521777153015137},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5270220041275024},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.46535947918891907},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.45918482542037964},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4515322148799896}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata59044.2023.10386783","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata59044.2023.10386783","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W2885212328","https://openalex.org/W2896457183","https://openalex.org/W2902699808","https://openalex.org/W2962716111","https://openalex.org/W2978017171","https://openalex.org/W3035391452","https://openalex.org/W3102373121","https://openalex.org/W3182513745","https://openalex.org/W4205757056","https://openalex.org/W4320024106","https://openalex.org/W4391092927","https://openalex.org/W6682762504","https://openalex.org/W6755207826","https://openalex.org/W6768851824"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W3204019825"],"abstract_inverted_index":{"In":[0,64,102],"the":[1,10,25,61,91,103,106,114,132],"domain":[2],"of":[3,28,36,76,88,149,161],"natural":[4],"language":[5],"processing":[6],"and":[7,58,94,139,158],"text":[8,97,108,164],"analysis,":[9],"Bidirectional":[11],"Encoder":[12],"Representations":[13],"from":[14,99,110],"Transformers":[15],"(BERT)":[16],"has":[17],"emerged":[18],"as":[19],"a":[20,40,155],"powerful":[21],"tool":[22],"for":[23,122,163],"discerning":[24],"intricate":[26],"nuances":[27],"textual":[29],"data.":[30,83],"Nonetheless,":[31],"BERT\u2019s":[32],"inherent":[33],"token":[34,133],"limit":[35],"512":[37],"tokens":[38],"presents":[39],"noteworthy":[41],"challenge":[42],"when":[43,145],"confronted":[44,146],"with":[45,147],"exceedingly":[46],"long":[47],"documents.":[48],"Lengthy":[49],"documents":[50,148],"are":[51],"commonly":[52],"encountered":[53],"during":[54],"legal":[55,82],"document":[56,93,112,129],"review":[57],"often":[59],"exceed":[60],"512-token":[62],"constraint.":[63],"response":[65],"to":[66,90,141,154],"this":[67,69],"challenge,":[68],"study":[70],"empirically":[71],"compares":[72,86],"two":[73],"distinct":[74],"applications":[75,87],"BERT,":[77],"leveraging":[78],"real-world,":[79],"construction":[80],"industry":[81],"The":[84],"approach":[85],"BERT":[89,125,162],"entire":[92],"on":[95],"segmented":[96],"portions":[98],"each":[100,111],"document.":[101],"latter":[104],"approach,":[105],"highest-scoring":[107],"segment":[109],"represents":[113],"document\u2019s":[115],"score.":[116],"This":[117],"research":[118],"offers":[119],"practical":[120],"insights":[121],"effectively":[123],"utilizing":[124],"in":[126],"scenarios":[127],"where":[128],"length":[130],"exceeds":[131],"limit.":[134],"Our":[135],"results":[136],"allow":[137],"practitioners":[138],"researchers":[140],"make":[142],"informed":[143],"choices":[144],"significant":[150],"length,":[151],"thus":[152],"contributing":[153],"more":[156],"effective":[157],"insightful":[159],"application":[160],"analysis.":[165]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}