{"id":"https://openalex.org/W2894747149","doi":"https://doi.org/10.1145/3209280.3209526","title":"Comparative Study between Traditional Machine Learning and Deep Learning Approaches for Text Classification","display_name":"Comparative Study between Traditional Machine Learning and Deep Learning Approaches for Text Classification","publication_year":2018,"publication_date":"2018-08-28","ids":{"openalex":"https://openalex.org/W2894747149","doi":"https://doi.org/10.1145/3209280.3209526","mag":"2894747149"},"language":"en","primary_location":{"id":"doi:10.1145/3209280.3209526","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3209280.3209526","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Symposium on Document Engineering 2018","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/A5005883066","display_name":"Cannannore Nidhi Narayana Kamath","orcid":null},"institutions":[{"id":"https://openalex.org/I153267046","display_name":"University of Kaiserslautern","ror":"https://ror.org/04zrf7b53","country_code":"DE","type":"education","lineage":["https://openalex.org/I153267046"]},{"id":"https://openalex.org/I33256026","display_name":"German Research Centre for Artificial Intelligence","ror":"https://ror.org/01ayc5b57","country_code":"DE","type":"funder","lineage":["https://openalex.org/I33256026"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Cannannore Nidhi Kamath","raw_affiliation_strings":["German Research Center for Artificial Intelligence (DFKI), University of Kaiserslautern"],"affiliations":[{"raw_affiliation_string":"German Research Center for Artificial Intelligence (DFKI), University of Kaiserslautern","institution_ids":["https://openalex.org/I33256026","https://openalex.org/I153267046"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112334801","display_name":"Syed Saqib Bukhari","orcid":null},"institutions":[{"id":"https://openalex.org/I153267046","display_name":"University of Kaiserslautern","ror":"https://ror.org/04zrf7b53","country_code":"DE","type":"education","lineage":["https://openalex.org/I153267046"]},{"id":"https://openalex.org/I33256026","display_name":"German Research Centre for Artificial Intelligence","ror":"https://ror.org/01ayc5b57","country_code":"DE","type":"funder","lineage":["https://openalex.org/I33256026"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Syed Saqib Bukhari","raw_affiliation_strings":["German Research Center for Artificial Intelligence (DFKI), University of Kaiserslautern"],"affiliations":[{"raw_affiliation_string":"German Research Center for Artificial Intelligence (DFKI), University of Kaiserslautern","institution_ids":["https://openalex.org/I33256026","https://openalex.org/I153267046"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101904182","display_name":"Andreas Dengel","orcid":"https://orcid.org/0000-0002-6100-8255"},"institutions":[{"id":"https://openalex.org/I33256026","display_name":"German Research Centre for Artificial Intelligence","ror":"https://ror.org/01ayc5b57","country_code":"DE","type":"funder","lineage":["https://openalex.org/I33256026"]},{"id":"https://openalex.org/I153267046","display_name":"University of Kaiserslautern","ror":"https://ror.org/04zrf7b53","country_code":"DE","type":"education","lineage":["https://openalex.org/I153267046"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Andreas Dengel","raw_affiliation_strings":["German Research Center for Artificial Intelligence (DFKI), University of Kaiserslautern"],"affiliations":[{"raw_affiliation_string":"German Research Center for Artificial Intelligence (DFKI), University of Kaiserslautern","institution_ids":["https://openalex.org/I33256026","https://openalex.org/I153267046"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5005883066"],"corresponding_institution_ids":["https://openalex.org/I153267046","https://openalex.org/I33256026"],"apc_list":null,"apc_paid":null,"fwci":4.9072,"has_fulltext":false,"cited_by_count":117,"citation_normalized_percentile":{"value":0.96081118,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"11"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9993000030517578,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9993000030517578,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9865000247955322,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9861999750137329,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.814734697341919},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7890181541442871},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.7537921071052551},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.7477454543113708},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5783202648162842},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5385026335716248},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5017125606536865},{"id":"https://openalex.org/keywords/raw-data","display_name":"Raw data","score":0.49762871861457825},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.46334701776504517},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.4611516296863556},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4415460228919983},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.36460986733436584}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.814734697341919},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7890181541442871},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7537921071052551},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.7477454543113708},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5783202648162842},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5385026335716248},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5017125606536865},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.49762871861457825},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.46334701776504517},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.4611516296863556},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4415460228919983},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36460986733436584},{"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:10.1145/3209280.3209526","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3209280.3209526","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Symposium on Document Engineering 2018","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5400000214576721,"id":"https://metadata.un.org/sdg/8","display_name":"Decent work and economic growth"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W171594045","https://openalex.org/W1832693441","https://openalex.org/W1923801961","https://openalex.org/W1965398296","https://openalex.org/W2101234009","https://openalex.org/W2140336868","https://openalex.org/W2149298154","https://openalex.org/W2151595407","https://openalex.org/W2251143283","https://openalex.org/W2465020480","https://openalex.org/W2559655401","https://openalex.org/W2911964244"],"related_works":["https://openalex.org/W4367336074","https://openalex.org/W3154045278","https://openalex.org/W4379620016","https://openalex.org/W4393666307","https://openalex.org/W3210764983","https://openalex.org/W4393443811","https://openalex.org/W4367335949","https://openalex.org/W3089416646","https://openalex.org/W4396816114","https://openalex.org/W4380048833"],"abstract_inverted_index":{"In":[0],"this":[1],"contemporaneous":[2],"world,":[3],"it":[4],"is":[5,27,114,122,165,178,188],"an":[6,73],"obligation":[7],"for":[8,52,84,111,149,203],"any":[9],"organization":[10,104],"working":[11],"with":[12,17,67,168,190,216],"documents":[13,47,66],"to":[14,124,181],"end":[15],"up":[16],"the":[18,28,34,118,183,212,228],"insipid":[19],"task":[20],"of":[21,24,31,36,44,55,72,151,192,205],"classifying":[22],"truckload":[23],"documents,":[25],"which":[26,62],"nascent":[29],"stage":[30],"venturing":[32],"into":[33,48],"realm":[35],"information":[37],"retrieval":[38],"and":[39,57,87,93,120,144,158,162,185,207,215,230],"data":[40,99,110,160,184,213],"mining.":[41],"But":[42],"classification":[43,86,150,204],"such":[45,173],"humongous":[46],"multiple":[49,82],"classes,":[50],"calls":[51],"a":[53,60,102],"lot":[54],"time":[56],"labor.":[58],"Hence":[59],"system":[61,210],"could":[63],"classify":[64,182],"these":[65],"acceptable":[68],"accuracy":[69,90,164,187,218],"would":[70],"be":[71],"unfathomable":[74],"help":[75],"in":[76,101,227],"document":[77,85],"engineering.":[78],"We":[79,96,197],"have":[80,97],"created":[81],"classifiers":[83,202],"compared":[88,189],"their":[89,163],"on":[91,156],"raw":[92,157],"processed":[94,115,159],"data.":[95],"garnered":[98],"used":[100,148,180],"corporate":[103],"as":[105,107,174],"well":[106],"publicly":[108],"available":[109],"comparison.":[112],"Data":[113],"by":[116],"removing":[117],"stop-words":[119],"stemming":[121],"implemented":[123],"produce":[125],"root":[126],"words.":[127],"Multiple":[128],"traditional":[129,193],"machine":[130,194],"learning":[131,171,195],"techniques":[132],"like":[133],"Naive":[134],"Bayes,":[135],"Logistic":[136],"Regression,":[137],"Support":[138],"Vector":[139],"Machine,":[140],"Random":[141],"forest":[142],"Classifier":[143],"Multi-Layer":[145],"Perceptron":[146],"are":[147,154,198,225],"documents.":[152],"Classifiers":[153],"applied":[155],"separately":[161],"noted.":[166],"Along":[167],"this,":[169],"Deep":[170],"technique":[172],"Convolution":[175],"Neural":[176],"Network":[177],"also":[179,199],"its":[186],"that":[191],"techniques.":[196],"exploring":[200],"hierarchical":[201],"classes":[206],"subclasses.":[208],"The":[209,223],"classifies":[211],"faster":[214],"better":[217],"than":[219],"if":[220],"done":[221],"manually.":[222],"results":[224,229],"discussed":[226],"evaluation":[231],"section.":[232]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":18},{"year":2024,"cited_by_count":26},{"year":2023,"cited_by_count":24},{"year":2022,"cited_by_count":18},{"year":2021,"cited_by_count":16},{"year":2020,"cited_by_count":9},{"year":2019,"cited_by_count":4}],"updated_date":"2026-04-20T07:46:08.049788","created_date":"2025-10-10T00:00:00"}