{"id":"https://openalex.org/W3181441981","doi":"https://doi.org/10.1145/3456529.3456556","title":"WordErrorSim: An Adversarial Examples Generation Method in Chinese by Erroneous Knowledge","display_name":"WordErrorSim: An Adversarial Examples Generation Method in Chinese by Erroneous Knowledge","publication_year":2021,"publication_date":"2021-02-02","ids":{"openalex":"https://openalex.org/W3181441981","doi":"https://doi.org/10.1145/3456529.3456556","mag":"3181441981"},"language":"en","primary_location":{"id":"doi:10.1145/3456529.3456556","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3456529.3456556","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 The 5th International Conference on Compute and Data Analysis","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/A5085868833","display_name":"Ran Jin","orcid":"https://orcid.org/0000-0003-3847-4538"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ran Jin","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047996469","display_name":"Chunhua Wu","orcid":"https://orcid.org/0000-0003-0535-6236"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chunhua Wu","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.09201151,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"155","last_page":"161"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9976000189781189,"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":0.9976000189781189,"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":0.9811000227928162,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9674999713897705,"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":0.8116515278816223},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.7899715304374695},{"id":"https://openalex.org/keywords/character","display_name":"Character (mathematics)","score":0.732508659362793},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6858696937561035},{"id":"https://openalex.org/keywords/pronunciation","display_name":"Pronunciation","score":0.6246341466903687},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5236382484436035},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5068114399909973},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.5049636960029602},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.4876810312271118},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.4739658534526825},{"id":"https://openalex.org/keywords/chinese-characters","display_name":"Chinese characters","score":0.4391739070415497},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4389355182647705},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.43784791231155396},{"id":"https://openalex.org/keywords/grammar","display_name":"Grammar","score":0.41142791509628296},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.1513533592224121},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.13699635863304138},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.10348063707351685},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.0842931866645813}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8116515278816223},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.7899715304374695},{"id":"https://openalex.org/C2780861071","wikidata":"https://www.wikidata.org/wiki/Q1062934","display_name":"Character (mathematics)","level":2,"score":0.732508659362793},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6858696937561035},{"id":"https://openalex.org/C2780844864","wikidata":"https://www.wikidata.org/wiki/Q184377","display_name":"Pronunciation","level":2,"score":0.6246341466903687},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5236382484436035},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5068114399909973},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.5049636960029602},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.4876810312271118},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.4739658534526825},{"id":"https://openalex.org/C2781051154","wikidata":"https://www.wikidata.org/wiki/Q8201","display_name":"Chinese characters","level":2,"score":0.4391739070415497},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4389355182647705},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.43784791231155396},{"id":"https://openalex.org/C26022165","wikidata":"https://www.wikidata.org/wiki/Q8091","display_name":"Grammar","level":2,"score":0.41142791509628296},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.1513533592224121},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.13699635863304138},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.10348063707351685},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0842931866645813},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3456529.3456556","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3456529.3456556","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 The 5th International Conference on Compute and Data Analysis","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","score":0.7799999713897705,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W1569400707","https://openalex.org/W1832693441","https://openalex.org/W2165945467","https://openalex.org/W2735135478","https://openalex.org/W2962718684","https://openalex.org/W2962818281","https://openalex.org/W2963918774","https://openalex.org/W3018666070","https://openalex.org/W3081017752","https://openalex.org/W4247950230","https://openalex.org/W4402683075"],"related_works":["https://openalex.org/W2502115930","https://openalex.org/W2482350142","https://openalex.org/W2183593636","https://openalex.org/W4246396837","https://openalex.org/W3126451824","https://openalex.org/W2356756483","https://openalex.org/W2348863460","https://openalex.org/W2355692578","https://openalex.org/W2529685800","https://openalex.org/W2547767923"],"abstract_inverted_index":{"Deep":[0],"learning":[1],"is":[2],"widely":[3],"used":[4,79],"in":[5,13,34],"many":[6],"kinds":[7],"of":[8,25,95,125,150],"network":[9],"applications,":[10],"but":[11],"errors":[12],"texts":[14],"challenge":[15],"the":[16,23,29,87,91,96,111,114,118,123,126,147,151],"model's":[17],"performance.":[18],"In":[19],"order":[20],"to":[21,57,109],"study":[22],"impact":[24],"erroneous":[26,59,80],"knowledge":[27,60],"on":[28,132],"Chinese":[30,104],"text":[31],"classification":[32,148],"model,":[33],"this":[35],"paper":[36],"we":[37,100],"propose":[38],"a":[39],"word-level":[40],"black":[41],"box":[42],"adversarial":[43,63,83,115],"example":[44,84,116],"generation":[45],"method":[46,128,143],"called":[47],"WordErrorSim.":[48],"The":[49,62,82,137],"algorithm":[50,108],"uses":[51],"SIGHAN":[52],"Back-off":[53],"2013":[54],"and":[55,102,117,134],"ZDIC":[56],"construct":[58],"space.":[61],"examples":[64],"generate":[65],"by":[66],"replacing":[67],"character":[68,75],"with":[69,129],"pronounce":[70],"similar":[71,74],"character,":[72],"shape":[73],"or":[76,93],"other":[77],"commonly":[78],"character.":[81],"can":[85,144],"achieve":[86],"attack":[88],"without":[89],"changing":[90],"semantic":[92],"grammar":[94],"original":[97,119],"sentence.":[98],"Besides,":[99],"design":[101],"implement":[103],"Pronunciation-Shape":[105],"Similarity":[106],"(CPSS)":[107],"measure":[110],"change":[112],"between":[113],"text.":[120],"We":[121],"verify":[122],"effectiveness":[124],"proposed":[127],"real":[130],"dataset":[131],"Bi-LSTM":[133],"TextCNN":[135],"models.":[136],"experiment":[138],"results":[139],"show":[140],"that":[141],"our":[142],"significantly":[145],"reduce":[146],"accuracy":[149],"model.":[152]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}