{"id":"https://openalex.org/W4282927409","doi":"https://doi.org/10.3390/e24050590","title":"Investigating Multi-Level Semantic Extraction with Squash Capsules for Short Text Classification","display_name":"Investigating Multi-Level Semantic Extraction with Squash Capsules for Short Text Classification","publication_year":2022,"publication_date":"2022-04-23","ids":{"openalex":"https://openalex.org/W4282927409","doi":"https://doi.org/10.3390/e24050590","pmid":"https://pubmed.ncbi.nlm.nih.gov/35626475"},"language":"en","primary_location":{"id":"doi:10.3390/e24050590","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e24050590","pdf_url":"https://www.mdpi.com/1099-4300/24/5/590/pdf?version=1650707212","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1099-4300/24/5/590/pdf?version=1650707212","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100336998","display_name":"Jing Li","orcid":"https://orcid.org/0000-0002-8044-2284"},"institutions":[{"id":"https://openalex.org/I92403157","display_name":"University of Science and Technology Beijing","ror":"https://ror.org/02egmk993","country_code":"CN","type":"education","lineage":["https://openalex.org/I92403157"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing Li","raw_affiliation_strings":["Beijing Key Laboratory of Knowledge Engineering for Materials Science, University of Science and Technology Beijing, Beijing 100083, China","School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Knowledge Engineering for Materials Science, University of Science and Technology Beijing, Beijing 100083, China","institution_ids":["https://openalex.org/I92403157"]},{"raw_affiliation_string":"School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China","institution_ids":["https://openalex.org/I92403157"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024869142","display_name":"Dezheng Zhang","orcid":"https://orcid.org/0000-0002-3456-5259"},"institutions":[{"id":"https://openalex.org/I92403157","display_name":"University of Science and Technology Beijing","ror":"https://ror.org/02egmk993","country_code":"CN","type":"education","lineage":["https://openalex.org/I92403157"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dezheng Zhang","raw_affiliation_strings":["Beijing Key Laboratory of Knowledge Engineering for Materials Science, University of Science and Technology Beijing, Beijing 100083, China","School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China"],"raw_orcid":"https://orcid.org/0000-0002-3456-5259","affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Knowledge Engineering for Materials Science, University of Science and Technology Beijing, Beijing 100083, China","institution_ids":["https://openalex.org/I92403157"]},{"raw_affiliation_string":"School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China","institution_ids":["https://openalex.org/I92403157"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086313562","display_name":"Aziguli Wulamu","orcid":"https://orcid.org/0000-0001-7228-7838"},"institutions":[{"id":"https://openalex.org/I92403157","display_name":"University of Science and Technology Beijing","ror":"https://ror.org/02egmk993","country_code":"CN","type":"education","lineage":["https://openalex.org/I92403157"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Aziguli Wulamu","raw_affiliation_strings":["Beijing Key Laboratory of Knowledge Engineering for Materials Science, University of Science and Technology Beijing, Beijing 100083, China","School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Knowledge Engineering for Materials Science, University of Science and Technology Beijing, Beijing 100083, China","institution_ids":["https://openalex.org/I92403157"]},{"raw_affiliation_string":"School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China","institution_ids":["https://openalex.org/I92403157"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5086313562"],"corresponding_institution_ids":["https://openalex.org/I92403157"],"apc_list":{"value":2000,"currency":"CHF","value_usd":2227},"apc_paid":{"value":2000,"currency":"CHF","value_usd":2227},"fwci":0.4645,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.67922458,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"24","issue":"5","first_page":"590","last_page":"590"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9983999729156494,"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":0.9983999729156494,"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":0.9973999857902527,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9939000010490417,"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":0.8231673240661621},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6219156980514526},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6199005246162415},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.47302699089050293},{"id":"https://openalex.org/keywords/semantic-feature","display_name":"Semantic feature","score":0.461958646774292},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4360724687576294},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4272368550300598},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.41690096259117126},{"id":"https://openalex.org/keywords/normalization","display_name":"Normalization (sociology)","score":0.4136473834514618},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.4131910800933838}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8231673240661621},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6219156980514526},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6199005246162415},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.47302699089050293},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.461958646774292},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4360724687576294},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4272368550300598},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.41690096259117126},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.4136473834514618},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4131910800933838},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C19165224","wikidata":"https://www.wikidata.org/wiki/Q23404","display_name":"Anthropology","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.3390/e24050590","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e24050590","pdf_url":"https://www.mdpi.com/1099-4300/24/5/590/pdf?version=1650707212","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},{"id":"pmid:35626475","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/35626475","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:doaj.org/article:10d4ddddf1bd4d548977024363c8bec6","is_oa":true,"landing_page_url":"https://doaj.org/article/10d4ddddf1bd4d548977024363c8bec6","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":"Entropy, Vol 24, Iss 5, p 590 (2022)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/1099-4300/24/5/590/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/e24050590","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy; Volume 24; Issue 5; Pages: 590","raw_type":"Text"},{"id":"pmh:oai:pubmedcentral.nih.gov:9141385","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9141385","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy (Basel)","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/e24050590","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e24050590","pdf_url":"https://www.mdpi.com/1099-4300/24/5/590/pdf?version=1650707212","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.7900000214576721,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4282927409.pdf","grobid_xml":"https://content.openalex.org/works/W4282927409.grobid-xml"},"referenced_works_count":50,"referenced_works":["https://openalex.org/W1832693441","https://openalex.org/W2114524997","https://openalex.org/W2166706824","https://openalex.org/W2556096924","https://openalex.org/W2569656908","https://openalex.org/W2622365670","https://openalex.org/W2786396726","https://openalex.org/W2788615138","https://openalex.org/W2796138868","https://openalex.org/W2885141472","https://openalex.org/W2897402719","https://openalex.org/W2908173863","https://openalex.org/W2918540534","https://openalex.org/W2924354574","https://openalex.org/W2948073857","https://openalex.org/W2950404230","https://openalex.org/W2965833086","https://openalex.org/W2970398671","https://openalex.org/W2977233821","https://openalex.org/W2991113277","https://openalex.org/W2994261280","https://openalex.org/W2995824694","https://openalex.org/W3001230617","https://openalex.org/W3021053579","https://openalex.org/W3021453518","https://openalex.org/W3023672669","https://openalex.org/W3025887701","https://openalex.org/W3035568641","https://openalex.org/W3094067205","https://openalex.org/W3124539583","https://openalex.org/W3135051187","https://openalex.org/W3159075545","https://openalex.org/W3160409149","https://openalex.org/W3168972675","https://openalex.org/W3174820758","https://openalex.org/W3179515446","https://openalex.org/W3184110467","https://openalex.org/W3197116584","https://openalex.org/W3198277925","https://openalex.org/W3216864420","https://openalex.org/W4206256100","https://openalex.org/W4206469492","https://openalex.org/W4207064056","https://openalex.org/W4207078300","https://openalex.org/W4295765086","https://openalex.org/W6668254845","https://openalex.org/W6739901393","https://openalex.org/W6743446608","https://openalex.org/W6796589177","https://openalex.org/W6806768336"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W2745001401","https://openalex.org/W4321353415","https://openalex.org/W2130974462","https://openalex.org/W2028665553","https://openalex.org/W2086519370","https://openalex.org/W972276598","https://openalex.org/W2591697403","https://openalex.org/W2087343574","https://openalex.org/W4246352526"],"abstract_inverted_index":{"At":[0],"present,":[1],"short":[2,23,28,48,67,154],"text":[3,29,49,68,155],"classification":[4,30,42,203],"is":[5],"a":[6,40,72,158],"hot":[7],"topic":[8],"in":[9],"the":[10,18,25,45,81,85,93,99,107,116,119,122,127,146,180],"area":[11],"of":[12,22,27,47,106,113,121,149,182],"natural":[13],"language":[14],"processing.":[15],"Due":[16],"to":[17,62,167],"sparseness":[19],"and":[20,54,70,88,103,138,186,192,205],"irregularity":[21],"text,":[24],"task":[26],"still":[31],"faces":[32],"great":[33],"challenges.":[34],"In":[35,142],"this":[36],"paper,":[37],"we":[38,97,130,144],"propose":[39],"new":[41],"model":[43,171,199,206],"from":[44,66],"aspects":[46],"representation,":[50],"global":[51],"feature":[52,56,101,114,123,151],"extraction":[53,75,152],"local":[55,133],"extraction.":[57],"We":[58,162],"use":[59],"convolutional":[60],"networks":[61],"extract":[63],"shallow":[64],"features":[65],"vectorization,":[69],"introduce":[71],"multi-level":[73,159],"semantic":[74,104,108,150,160],"framework.":[76,109,161],"It":[77],"uses":[78],"BiLSTM":[79],"as":[80,92],"encoding":[82],"layer":[83],"while":[84],"attention":[86],"mechanism":[87],"normalization":[89],"are":[90,188],"used":[91],"interaction":[94],"layer.":[95],"Finally,":[96],"concatenate":[98],"convolution":[100],"vector":[102],"results":[105,177],"After":[110],"several":[111],"rounds":[112],"integration,":[115],"framework":[117],"improves":[118,202],"quality":[120],"representation.":[124],"Combined":[125],"with":[126],"capsule":[128],"network,":[129],"obtain":[131],"high-level":[132],"information":[134],"by":[135],"dynamic":[136],"routing":[137],"then":[139],"squash":[140],"them.":[141],"addition,":[143],"explore":[145],"optimal":[147],"depth":[148],"for":[153],"based":[156],"on":[157],"utilized":[163],"four":[164],"benchmark":[165],"datasets":[166],"demonstrate":[168],"that":[169,179,197],"our":[170,198],"provides":[172],"comparable":[173],"results.":[174],"The":[175],"experimental":[176],"show":[178],"accuracy":[181,204],"SUBJ,":[183],"TREC,":[184],"MR":[185],"ProcCons":[187],"93.8%,":[189],"91.94%,":[190],"82.81%":[191],"98.43%,":[193],"respectively,":[194],"which":[195],"verifies":[196],"has":[200],"greatly":[201],"robustness.":[207]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-30T17:31:21.811387","created_date":"2025-10-10T00:00:00"}
