{"id":"https://openalex.org/W2808133401","doi":"https://doi.org/10.24963/ijcai.2018/621","title":"Densely Connected CNN with Multi-scale Feature Attention for Text Classification","display_name":"Densely Connected CNN with Multi-scale Feature Attention for Text Classification","publication_year":2018,"publication_date":"2018-07-01","ids":{"openalex":"https://openalex.org/W2808133401","doi":"https://doi.org/10.24963/ijcai.2018/621","mag":"2808133401"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2018/621","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2018/621","pdf_url":"https://www.ijcai.org/proceedings/2018/0621.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.ijcai.org/proceedings/2018/0621.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5114585213","display_name":"Shiyao Wang","orcid":"https://orcid.org/0000-0001-5291-4945"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shiyao Wang","raw_affiliation_strings":["State Key Laboratory of Intelligent Technology and Systems, Beijing National Research Center for Information Science and Technology, Department of Computer Science, Tsinghua University, Beijing 100084, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Intelligent Technology and Systems, Beijing National Research Center for Information Science and Technology, Department of Computer Science, Tsinghua University, Beijing 100084, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044042138","display_name":"Minlie Huang","orcid":"https://orcid.org/0000-0001-7111-1849"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Minlie Huang","raw_affiliation_strings":["State Key Laboratory of Intelligent Technology and Systems, Beijing National Research Center for Information Science and Technology, Department of Computer Science, Tsinghua University, Beijing 100084, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Intelligent Technology and Systems, Beijing National Research Center for Information Science and Technology, Department of Computer Science, Tsinghua University, Beijing 100084, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102011846","display_name":"Zhidong Deng","orcid":"https://orcid.org/0000-0001-9970-1023"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhidong Deng","raw_affiliation_strings":["State Key Laboratory of Intelligent Technology and Systems, Beijing National Research Center for Information Science and Technology, Department of Computer Science, Tsinghua University, Beijing 100084, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Intelligent Technology and Systems, Beijing National Research Center for Information Science and Technology, Department of Computer Science, Tsinghua University, Beijing 100084, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5044042138"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":179,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4468","last_page":"4474"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9991999864578247,"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":0.998199999332428,"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":0.8091503381729126},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7344828844070435},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6662415266036987},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6600086688995361},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6330000162124634},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5755451321601868},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.563198983669281},{"id":"https://openalex.org/keywords/n-gram","display_name":"n-gram","score":0.5280390977859497},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5200238227844238},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4377063810825348},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.429358571767807},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.29000064730644226},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.24165505170822144}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8091503381729126},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7344828844070435},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6662415266036987},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6600086688995361},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6330000162124634},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5755451321601868},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.563198983669281},{"id":"https://openalex.org/C117884012","wikidata":"https://www.wikidata.org/wiki/Q94489","display_name":"n-gram","level":3,"score":0.5280390977859497},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5200238227844238},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4377063810825348},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.429358571767807},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.29000064730644226},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.24165505170822144},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2018/621","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2018/621","pdf_url":"https://www.ijcai.org/proceedings/2018/0621.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.24963/ijcai.2018/621","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2018/621","pdf_url":"https://www.ijcai.org/proceedings/2018/0621.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.7900000214576721,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[{"id":"https://openalex.org/G1522390346","display_name":null,"funder_award_id":"2017YFB1302200","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G4260095629","display_name":"\u5927\u89c4\u6a21\u529f\u80fd\u67f1\u96c6\u7fa4\u9012\u5f52\u795e\u7ecf\u7f51\u7edc\u6a21\u578b\u7684\u7406\u8bba\u3001\u65b9\u6cd5\u4e0e\u5e94\u7528\u7814\u7a76","funder_award_id":"60775040","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5016123089","display_name":null,"funder_award_id":"90820305","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8015682759","display_name":null,"funder_award_id":"91420106","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320316083","display_name":"Tencent","ror":"https://ror.org/00hhjss72"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322392","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2808133401.pdf","grobid_xml":"https://content.openalex.org/works/W2808133401.grobid-xml"},"referenced_works_count":27,"referenced_works":["https://openalex.org/W1514535095","https://openalex.org/W1665214252","https://openalex.org/W1832693441","https://openalex.org/W1836465849","https://openalex.org/W1902237438","https://openalex.org/W2120615054","https://openalex.org/W2170240176","https://openalex.org/W2194775991","https://openalex.org/W2211192759","https://openalex.org/W2250539671","https://openalex.org/W2252335727","https://openalex.org/W2413904250","https://openalex.org/W2470673105","https://openalex.org/W2597655663","https://openalex.org/W2622365670","https://openalex.org/W2740721704","https://openalex.org/W2741271950","https://openalex.org/W2785128315","https://openalex.org/W2950094539","https://openalex.org/W2952186591","https://openalex.org/W2952230511","https://openalex.org/W2963355447","https://openalex.org/W2963446712","https://openalex.org/W2963626623","https://openalex.org/W2963921497","https://openalex.org/W4292012835","https://openalex.org/W4294238563"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W4321353415","https://openalex.org/W2745001401","https://openalex.org/W2130974462","https://openalex.org/W2028665553","https://openalex.org/W2086519370","https://openalex.org/W4246352526","https://openalex.org/W2121910908","https://openalex.org/W915438175","https://openalex.org/W2964954556"],"abstract_inverted_index":{"Text":[0],"classification":[1],"is":[2,101],"a":[3,11,52,97],"fundamental":[4],"problem":[5],"in":[6,24],"natural":[7],"language":[8],"processing.":[9],"As":[10],"popular":[12],"deep":[13],"learning":[14],"model,":[15],"convolutional":[16,73],"neural":[17],"network":[18],"(CNN)":[19],"has":[20],"demonstrated":[21],"great":[22],"success":[23],"this":[25,48],"task.":[26],"However,":[27],"most":[28],"existing":[29],"CNN":[30,55],"models":[31],"apply":[32],"convolution":[33],"filters":[34],"of":[35,82,87],"fixed":[36],"window":[37],"size,":[38],"thereby":[39],"unable":[40],"to":[41,79,103,133],"learn":[42],"variable":[43,93],"n-gram":[44,94,136],"features":[45,81,107,137],"flexibly.":[46],"In":[47],"paper,":[49],"we":[50],"present":[51],"densely":[53],"connected":[54],"with":[56],"multi-scale":[57,98,106],"feature":[58,99],"attention":[59,100],"for":[60,108,138],"text":[61,139],"classification.":[62,109,140],"The":[63],"dense":[64],"connections":[65],"build":[66],"short-cut":[67],"paths":[68],"between":[69],"upstream":[70],"and":[71,90],"downstream":[72],"blocks,":[74],"which":[75],"enable":[76],"the":[77,130],"model":[78,115],"compose":[80],"larger":[83],"scale":[84],"from":[85],"those":[86],"smaller":[88],"scale,":[89],"thus":[91],"produce":[92],"features.":[95],"Furthermore,":[96],"developed":[102],"adaptively":[104],"select":[105,134],"Extensive":[110],"experiments":[111],"demonstrate":[112],"that":[113],"our":[114],"obtains":[116],"competitive":[117],"performance":[118],"against":[119],"state-of-the-art":[120],"baselines":[121],"on":[122],"five":[123],"benchmark":[124],"datasets.":[125],"Attention":[126],"visualization":[127],"further":[128],"reveals":[129],"model's":[131],"ability":[132],"proper":[135]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":22},{"year":2023,"cited_by_count":22},{"year":2022,"cited_by_count":38},{"year":2021,"cited_by_count":35},{"year":2020,"cited_by_count":28},{"year":2019,"cited_by_count":19},{"year":2018,"cited_by_count":3}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
