{"id":"https://openalex.org/W4312323315","doi":"https://doi.org/10.1109/access.2022.3210697","title":"DPNPED: Dynamic Perception Network for Polysemous Event Trigger Detection","display_name":"DPNPED: Dynamic Perception Network for Polysemous Event Trigger Detection","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4312323315","doi":"https://doi.org/10.1109/access.2022.3210697"},"language":"en","primary_location":{"id":"doi:10.1109/access.2022.3210697","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3210697","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09905582.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09905582.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5030552559","display_name":"Jun Xu","orcid":"https://orcid.org/0000-0001-9565-6106"},"institutions":[{"id":"https://openalex.org/I4210090985","display_name":"Zhejiang Financial College","ror":"https://ror.org/00deghz86","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210090985"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jun Xu","raw_affiliation_strings":["Ant Financial Services Group, Zhejiang, Hangzhou, China","Ant Financial Services Group, Hangzhou, Zhejiang, China"],"raw_orcid":"https://orcid.org/0000-0001-9565-6106","affiliations":[{"raw_affiliation_string":"Ant Financial Services Group, Zhejiang, Hangzhou, China","institution_ids":["https://openalex.org/I4210090985"]},{"raw_affiliation_string":"Ant Financial Services Group, Hangzhou, Zhejiang, China","institution_ids":["https://openalex.org/I4210090985"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022327195","display_name":"Mengshu Sun","orcid":"https://orcid.org/0000-0003-2639-9462"},"institutions":[{"id":"https://openalex.org/I4210090985","display_name":"Zhejiang Financial College","ror":"https://ror.org/00deghz86","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210090985"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mengshu Sun","raw_affiliation_strings":["Ant Financial Services Group, Zhejiang, Hangzhou, China","Ant Financial Services Group, Hangzhou, Zhejiang, China"],"raw_orcid":"https://orcid.org/0000-0003-2639-9462","affiliations":[{"raw_affiliation_string":"Ant Financial Services Group, Zhejiang, Hangzhou, China","institution_ids":["https://openalex.org/I4210090985"]},{"raw_affiliation_string":"Ant Financial Services Group, Hangzhou, Zhejiang, China","institution_ids":["https://openalex.org/I4210090985"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5030552559"],"corresponding_institution_ids":["https://openalex.org/I4210090985"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.1426,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.5143909,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"10","issue":null,"first_page":"104801","last_page":"104810"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.9969000220298767,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9969000220298767,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T11719","display_name":"Data Quality and Management","score":0.9955000281333923,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9943000078201294,"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/polysemy","display_name":"Polysemy","score":0.8537333011627197},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8119656443595886},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.7232717275619507},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6485410928726196},{"id":"https://openalex.org/keywords/categorization","display_name":"Categorization","score":0.6176980137825012},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5750784277915955},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.5695971250534058},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4802734851837158},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4124758541584015},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39340299367904663},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3821560740470886},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3477014899253845}],"concepts":[{"id":"https://openalex.org/C2780276568","wikidata":"https://www.wikidata.org/wiki/Q191928","display_name":"Polysemy","level":2,"score":0.8537333011627197},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8119656443595886},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.7232717275619507},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6485410928726196},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.6176980137825012},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5750784277915955},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.5695971250534058},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4802734851837158},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4124758541584015},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39340299367904663},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3821560740470886},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3477014899253845},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"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/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2022.3210697","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3210697","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09905582.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:e47a3746af3b47a7998a2ae8f613f7f9","is_oa":true,"landing_page_url":"https://doaj.org/article/e47a3746af3b47a7998a2ae8f613f7f9","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":"IEEE Access, Vol 10, Pp 104801-104810 (2022)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2022.3210697","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3210697","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09905582.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.4099999964237213,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320330001","display_name":"Ant Financial Services Group","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4312323315.pdf","grobid_xml":"https://content.openalex.org/works/W4312323315.grobid-xml"},"referenced_works_count":55,"referenced_works":["https://openalex.org/W2033178790","https://openalex.org/W2314589280","https://openalex.org/W2325237720","https://openalex.org/W2475245295","https://openalex.org/W2512007443","https://openalex.org/W2606492274","https://openalex.org/W2739542029","https://openalex.org/W2798636921","https://openalex.org/W2806146112","https://openalex.org/W2888881387","https://openalex.org/W2890373807","https://openalex.org/W2891553865","https://openalex.org/W2946760275","https://openalex.org/W2947187520","https://openalex.org/W2948947170","https://openalex.org/W2951001719","https://openalex.org/W2951287298","https://openalex.org/W2952361887","https://openalex.org/W2952429226","https://openalex.org/W2963351448","https://openalex.org/W2964206023","https://openalex.org/W2970763364","https://openalex.org/W2970950715","https://openalex.org/W2971104547","https://openalex.org/W2989824617","https://openalex.org/W3006009210","https://openalex.org/W3035000929","https://openalex.org/W3035030897","https://openalex.org/W3044438666","https://openalex.org/W3098881736","https://openalex.org/W3101701554","https://openalex.org/W3105816068","https://openalex.org/W3106098584","https://openalex.org/W3110137956","https://openalex.org/W3112673818","https://openalex.org/W3131870090","https://openalex.org/W3173506858","https://openalex.org/W3173563887","https://openalex.org/W3174130957","https://openalex.org/W3176187859","https://openalex.org/W3193542387","https://openalex.org/W6679844565","https://openalex.org/W6698813593","https://openalex.org/W6701498492","https://openalex.org/W6739901393","https://openalex.org/W6741753902","https://openalex.org/W6751913510","https://openalex.org/W6753640285","https://openalex.org/W6754318665","https://openalex.org/W6755207826","https://openalex.org/W6768021236","https://openalex.org/W6769227307","https://openalex.org/W6783990618","https://openalex.org/W6786899916","https://openalex.org/W6791055906"],"related_works":["https://openalex.org/W2376040010","https://openalex.org/W2613880225","https://openalex.org/W2788559978","https://openalex.org/W2358036664","https://openalex.org/W2891304714","https://openalex.org/W4385239993","https://openalex.org/W2310152915","https://openalex.org/W2362895247","https://openalex.org/W4285531126","https://openalex.org/W2353607782"],"abstract_inverted_index":{"Event":[0],"detection":[1],"is":[2,51,94],"the":[3,11,37,44,78,89,91,98,108,117,122,137,145],"process":[4],"of":[5,13,46,100,147],"analyzing":[6],"event":[7,29,48,105,163],"streams":[8],"to":[9,25,60,87,114,134,143],"detect":[10],"occurrences":[12],"events":[14],"and":[15,27,39,58,83,124,140],"categorize":[16],"them.":[17],"General":[18],"methods":[19],"for":[20],"solving":[21],"this":[22,67],"problem":[23],"are":[24],"identify":[26],"classify":[28],"triggers.":[30,49,149],"Most":[31],"previous":[32],"works":[33],"focused":[34],"on":[35,97],"improving":[36],"recognition":[38],"classification":[40],"networks":[41],"which":[42,75],"neglected":[43],"representation":[45],"polysemous":[47,64,82,148,162],"Polysemy":[50],"habitually":[52],"somewhat":[53],"confusing":[54],"in":[55,161],"semantic":[56],"understanding":[57],"hard":[59],"detect.":[61],"To":[62],"improve":[63],"trigger":[65,164],"detection,":[66],"paper":[68],"proposes":[69],"a":[70,101,111,158],"novel":[71],"framework":[72],"called":[73],"DPNPED,":[74],"dynamically":[76,135],"adjusts":[77],"network":[79,118],"depth":[80,119],"between":[81],"common":[84],"words.":[85],"Firstly,":[86],"measure":[88,113,142],"polysemy,":[90],"difficulty":[92,138],"factor":[93,139],"devised":[95],"based":[96],"frequency":[99],"word":[102],"as":[103],"an":[104],"trigger.":[106],"Secondly,":[107],"DPNPED":[109],"utilizes":[110],"confidence":[112,141],"automatically":[115],"adjust":[116],"by":[120],"comparing":[121],"predicted":[123],"initial":[125],"probability":[126],"distribution.":[127],"Finally,":[128],"our":[129,155],"model":[130],"applies":[131],"focal":[132],"loss":[133],"integrate":[136],"enhance":[144],"learning":[146],"The":[150],"experimental":[151],"results":[152],"show":[153],"that":[154],"method":[156],"achieves":[157],"noticeable":[159],"improvement":[160],"detection.":[165]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}