{"id":"https://openalex.org/W3048675253","doi":"https://doi.org/10.1145/3408066.3408067","title":"A Convolutional Neural Network with Word-level Attention for Text Classification","display_name":"A Convolutional Neural Network with Word-level Attention for Text Classification","publication_year":2020,"publication_date":"2020-06-22","ids":{"openalex":"https://openalex.org/W3048675253","doi":"https://doi.org/10.1145/3408066.3408067","mag":"3048675253"},"language":"en","primary_location":{"id":"doi:10.1145/3408066.3408067","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3408066.3408067","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 12th International Conference on Computer Modeling and Simulation","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/A5038663771","display_name":"Baihan Kang","orcid":null},"institutions":[{"id":"https://openalex.org/I196699116","display_name":"Wuhan University of Technology","ror":"https://ror.org/03fe7t173","country_code":"CN","type":"education","lineage":["https://openalex.org/I196699116"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Baihan Kang","raw_affiliation_strings":["Wuhan University of Technology, Wuhan, Hubei, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Wuhan University of Technology, Wuhan, Hubei, China","institution_ids":["https://openalex.org/I196699116"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5038663771"],"corresponding_institution_ids":["https://openalex.org/I196699116"],"apc_list":null,"apc_paid":null,"fwci":0.23,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.48572026,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"19","last_page":"24"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9994999766349792,"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":0.9994999766349792,"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":0.9994000196456909,"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":0.9988999962806702,"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":0.7868150472640991},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.750796914100647},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.616905152797699},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.6131216287612915},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5957518815994263},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.12793287634849548}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7868150472640991},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.750796914100647},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.616905152797699},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.6131216287612915},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5957518815994263},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.12793287634849548},{"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":"doi:10.1145/3408066.3408067","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3408066.3408067","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 12th International Conference on Computer Modeling and Simulation","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.7799999713897705}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1550206324","https://openalex.org/W1676314349","https://openalex.org/W1832693441","https://openalex.org/W1956559956","https://openalex.org/W1969572066","https://openalex.org/W2053463056","https://openalex.org/W2064675550","https://openalex.org/W2112796928","https://openalex.org/W2136922672","https://openalex.org/W2149684865","https://openalex.org/W2153579005","https://openalex.org/W2158899491","https://openalex.org/W2163922914","https://openalex.org/W2250539671","https://openalex.org/W2250966211","https://openalex.org/W2882319491","https://openalex.org/W2963355447","https://openalex.org/W2964121744","https://openalex.org/W4230872509","https://openalex.org/W6683567821","https://openalex.org/W6812014127"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4293226380","https://openalex.org/W2382290278","https://openalex.org/W2478288626","https://openalex.org/W4391913857","https://openalex.org/W3204019825"],"abstract_inverted_index":{"Text":[0],"classification":[1,60],"is":[2,46,61,96],"a":[3,88],"classic":[4,116],"task":[5,27,45],"in":[6,68,83,107],"the":[7,14,29,32,38,44,47,53,65,69,76,80,115,124,131,137],"NLP":[8],"area":[9],"which":[10,71,95],"aims":[11],"to":[12,25,64,98,102,105],"predict":[13],"categories":[15],"for":[16,59],"given":[17],"texts.":[18,81,109],"Many":[19],"neural":[20,33,40,49,92],"network":[21,34,41,50,93],"models":[22],"are":[23],"applied":[24],"this":[26,84],"with":[28],"development":[30],"of":[31,37,126],"technology.":[35],"One":[36],"typical":[39],"structures":[42],"on":[43,119],"convolutional":[48,91],"(CNN).":[51],"However,":[52],"existing":[54],"traditional":[55],"CNN":[56,117,139],"model":[57,113,118,135],"used":[58],"not":[62],"sensitive":[63],"key":[66],"words":[67,104],"texts,":[70],"causes":[72],"it":[73],"cannot":[74],"capture":[75],"important":[77,103],"information":[78],"from":[79],"Therefore,":[82],"paper,":[85],"we":[86],"propose":[87],"new":[89,132],"attention-based":[90],"(ACNN)":[94],"trained":[97],"pay":[99],"more":[100],"attention":[101],"assist":[106],"classifying":[108],"We":[110],"evaluate":[111],"our":[112,127],"and":[114,123],"several":[120],"public":[121],"datasets,":[122],"result":[125],"experiment":[128],"shows":[129],"that":[130],"proposed":[133],"ACNN":[134],"outperforms":[136],"basic":[138],"model.":[140]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}