{"id":"https://openalex.org/W2603666613","doi":"https://doi.org/10.1186/s40537-017-0065-8","title":"Improving deep neural network design with new text data representations","display_name":"Improving deep neural network design with new text data representations","publication_year":2017,"publication_date":"2017-03-28","ids":{"openalex":"https://openalex.org/W2603666613","doi":"https://doi.org/10.1186/s40537-017-0065-8","mag":"2603666613"},"language":"en","primary_location":{"id":"doi:10.1186/s40537-017-0065-8","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-017-0065-8","pdf_url":"https://journalofbigdata.springeropen.com/track/pdf/10.1186/s40537-017-0065-8","source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Big Data","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://journalofbigdata.springeropen.com/track/pdf/10.1186/s40537-017-0065-8","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5085551052","display_name":"Joseph D. Prusa","orcid":null},"institutions":[{"id":"https://openalex.org/I63772739","display_name":"Florida Atlantic University","ror":"https://ror.org/05p8w6387","country_code":"US","type":"education","lineage":["https://openalex.org/I63772739"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Joseph D. Prusa","raw_affiliation_strings":["Florida Atlantic University, 777 Glades Road, Boca Raton, FL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Florida Atlantic University, 777 Glades Road, Boca Raton, FL, USA","institution_ids":["https://openalex.org/I63772739"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089170562","display_name":"Taghi M. Khoshgoftaar","orcid":null},"institutions":[{"id":"https://openalex.org/I63772739","display_name":"Florida Atlantic University","ror":"https://ror.org/05p8w6387","country_code":"US","type":"education","lineage":["https://openalex.org/I63772739"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Taghi M. Khoshgoftaar","raw_affiliation_strings":["Florida Atlantic University, 777 Glades Road, Boca Raton, FL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Florida Atlantic University, 777 Glades Road, Boca Raton, FL, USA","institution_ids":["https://openalex.org/I63772739"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5085551052"],"corresponding_institution_ids":["https://openalex.org/I63772739"],"apc_list":{"value":1060,"currency":"GBP","value_usd":1300},"apc_paid":{"value":1060,"currency":"GBP","value_usd":1300},"fwci":5.1631,"has_fulltext":true,"cited_by_count":67,"citation_normalized_percentile":{"value":0.96288876,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":"4","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9998999834060669,"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":0.9998999834060669,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9995999932289124,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9976999759674072,"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":0.8578498363494873},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"Feature engineering","score":0.8242586851119995},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7674298286437988},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7185646891593933},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7085549831390381},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6420218348503113},{"id":"https://openalex.org/keywords/character","display_name":"Character (mathematics)","score":0.6258039474487305},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.550728440284729},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5403810143470764},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.531577467918396},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4930886924266815},{"id":"https://openalex.org/keywords/word-embedding","display_name":"Word embedding","score":0.47869423031806946},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.4773692786693573},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4333184063434601}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8578498363494873},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.8242586851119995},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7674298286437988},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7185646891593933},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7085549831390381},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6420218348503113},{"id":"https://openalex.org/C2780861071","wikidata":"https://www.wikidata.org/wiki/Q1062934","display_name":"Character (mathematics)","level":2,"score":0.6258039474487305},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.550728440284729},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5403810143470764},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.531577467918396},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4930886924266815},{"id":"https://openalex.org/C2777462759","wikidata":"https://www.wikidata.org/wiki/Q18395344","display_name":"Word embedding","level":3,"score":0.47869423031806946},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.4773692786693573},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4333184063434601},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"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/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1186/s40537-017-0065-8","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-017-0065-8","pdf_url":"https://journalofbigdata.springeropen.com/track/pdf/10.1186/s40537-017-0065-8","source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Big Data","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:49e1bc99364340c0ba26d577848d51db","is_oa":true,"landing_page_url":"https://doaj.org/article/49e1bc99364340c0ba26d577848d51db","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Journal of Big Data, Vol 4, Iss 1, Pp 1-16 (2017)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1186/s40537-017-0065-8","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-017-0065-8","pdf_url":"https://journalofbigdata.springeropen.com/track/pdf/10.1186/s40537-017-0065-8","source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Big Data","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.6100000143051147}],"awards":[{"id":"https://openalex.org/G1444267946","display_name":null,"funder_award_id":"NS-1427536","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5416735113","display_name":"MRI: Acquisition of Big Data Training and Research Laboratory","funder_award_id":"1427536","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6293531692","display_name":null,"funder_award_id":"CNS-1427536","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2603666613.pdf","grobid_xml":"https://content.openalex.org/works/W2603666613.grobid-xml"},"referenced_works_count":27,"referenced_works":["https://openalex.org/W183625566","https://openalex.org/W615842445","https://openalex.org/W1165382972","https://openalex.org/W1614298861","https://openalex.org/W1665214252","https://openalex.org/W1667652561","https://openalex.org/W1743243001","https://openalex.org/W1775434803","https://openalex.org/W1832693441","https://openalex.org/W1839863673","https://openalex.org/W2028499920","https://openalex.org/W2095705004","https://openalex.org/W2097117768","https://openalex.org/W2117130368","https://openalex.org/W2118023920","https://openalex.org/W2152175008","https://openalex.org/W2162931300","https://openalex.org/W2163605009","https://openalex.org/W2250966211","https://openalex.org/W2251394420","https://openalex.org/W2267835966","https://openalex.org/W2314199904","https://openalex.org/W2403344661","https://openalex.org/W2911988977","https://openalex.org/W2950075229","https://openalex.org/W3138798301","https://openalex.org/W4249179470"],"related_works":["https://openalex.org/W2726375170","https://openalex.org/W4286432911","https://openalex.org/W2785740378","https://openalex.org/W4390421161","https://openalex.org/W2944541365","https://openalex.org/W3199964822","https://openalex.org/W4387022695","https://openalex.org/W4232132981","https://openalex.org/W4238046985","https://openalex.org/W2912503608"],"abstract_inverted_index":{"Using":[0],"traditional":[1],"machine":[2],"learning":[3,17,37,47,246],"approaches,":[4],"there":[5],"is":[6],"no":[7],"single":[8],"feature":[9,27,75],"engineering":[10,28],"solution":[11],"for":[12,30,64,79,245],"all":[13],"text":[14,32,53,114,176],"mining":[15],"and":[16,23,46,77,150,159,202,238],"tasks.":[18],"Thus,":[19],"researchers":[20],"must":[21],"determine":[22],"implement":[24],"the":[25,119,128,144,179,226,242],"best":[26],"approach":[29,104,244],"each":[31],"classification":[33,78,208],"task;":[34],"however,":[35,143],"deep":[36,65,186],"allows":[38],"us":[39],"to":[40,70,95,109,118,177,224],"skip":[41],"this":[42,164],"step":[43],"by":[44],"extracting":[45],"high-level":[48],"features":[49],"automatically":[50],"from":[51,98,127,133,247],"low-level":[52],"representations.":[54],"Convolutional":[55],"neural":[56,62,90,131,188],"networks,":[57],"a":[58,138,157,168,185],"popular":[59],"type":[60],"of":[61,101,121,141,147,171,175,195,228],"network":[63,231],"learning,":[66],"have":[67],"been":[68],"shown":[69],"be":[71,93,218],"effective":[72],"at":[73],"performing":[74],"extraction":[76],"many":[80],"domains":[81],"including":[82],"text.":[83,102,249],"Recently,":[84],"it":[85],"was":[86],"demonstrated":[87],"that":[88,192,213],"convolutional":[89,187,222,230],"networks":[91,132,156],"can":[92,217],"used":[94,219],"train":[96],"classifiers":[97,110],"character-level":[99,122,173,248],"representations":[100,123,174],"This":[103],"achieved":[105],"superior":[106],"performance":[107,240],"compared":[108],"trained":[111],"on":[112],"word-level":[113],"representations,":[115],"likely":[116],"due":[117],"use":[120,227],"preserving":[124],"more":[125],"information":[126],"data.":[129],"Training":[130],"character":[134,196],"level":[135],"data":[136,149],"requires":[137],"large":[139,145],"volume":[140,146],"instances;":[142],"training":[148,154,184,200,237],"model":[151],"complexity":[152],"makes":[153],"these":[155],"slow":[158],"computationally":[160],"expensive":[161],"task.":[162],"In":[163],"paper,":[165],"we":[166,211],"propose":[167],"new":[169],"method":[170,194],"creating":[172],"reduce":[178],"computational":[180],"costs":[181],"associated":[182],"with":[183,220],"network.":[189],"We":[190],"demonstrate":[191],"our":[193,214],"embedding":[197,216],"greatly":[198],"reduces":[199],"time":[201],"memory":[203],"use,":[204],"while":[205,233],"significantly":[206],"improving":[207],"performance.":[209],"Additionally,":[210],"show":[212],"proposed":[215],"padded":[221],"layers":[223],"enable":[225],"current":[229],"architectures,":[232],"still":[234],"facilitating":[235],"faster":[236],"higher":[239],"than":[241],"previous":[243]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":11},{"year":2021,"cited_by_count":15},{"year":2020,"cited_by_count":9},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":10},{"year":2017,"cited_by_count":3}],"updated_date":"2026-06-19T15:47:20.252518","created_date":"2025-10-10T00:00:00"}