{"id":"https://openalex.org/W2964401366","doi":"https://doi.org/10.24963/ijcai.2019/707","title":"Deep Mask Memory Network with Semantic Dependency and Context Moment for Aspect Level Sentiment Classification","display_name":"Deep Mask Memory Network with Semantic Dependency and Context Moment for Aspect Level Sentiment Classification","publication_year":2019,"publication_date":"2019-07-28","ids":{"openalex":"https://openalex.org/W2964401366","doi":"https://doi.org/10.24963/ijcai.2019/707","mag":"2964401366"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2019/707","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2019/707","pdf_url":"https://www.ijcai.org/proceedings/2019/0707.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Eighth 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/2019/0707.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5014957657","display_name":"Peiqin Lin","orcid":"https://orcid.org/0000-0003-2818-3008"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peiqin Lin","raw_affiliation_strings":["School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037873810","display_name":"Meng Yang","orcid":"https://orcid.org/0000-0002-0795-3221"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Meng Yang","raw_affiliation_strings":["Key Laboratory of Machine Intelligence and Advanced Computing(SYSU), Ministry of Education","School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China","Key Laboratory of Machine Intelligence and Advanced Computing(SYSU), Ministry of Education; School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Laboratory of Machine Intelligence and Advanced Computing(SYSU), Ministry of Education","institution_ids":[]},{"raw_affiliation_string":"School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]},{"raw_affiliation_string":"Key Laboratory of Machine Intelligence and Advanced Computing(SYSU), Ministry of Education; School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034685928","display_name":"Jianhuang Lai","orcid":"https://orcid.org/0000-0003-3883-2024"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianhuang Lai","raw_affiliation_strings":["School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I157773358"],"apc_list":null,"apc_paid":null,"fwci":3.827,"has_fulltext":false,"cited_by_count":54,"citation_normalized_percentile":{"value":0.95258901,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"5088","last_page":"5094"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9998999834060669,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9998999834060669,"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":0.9987000226974487,"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":0.9983999729156494,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8700392246246338},{"id":"https://openalex.org/keywords/semeval","display_name":"SemEval","score":0.7313003540039062},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.6976215839385986},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6704211831092834},{"id":"https://openalex.org/keywords/dependency","display_name":"Dependency (UML)","score":0.6574059128761292},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6185795068740845},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.6022210717201233},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.5662471652030945},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.5282360911369324},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.4920324981212616},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4612506628036499},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.4529784917831421},{"id":"https://openalex.org/keywords/parsing","display_name":"Parsing","score":0.4219944477081299},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.314656525850296},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.1323142945766449},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.08594244718551636}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8700392246246338},{"id":"https://openalex.org/C44572571","wikidata":"https://www.wikidata.org/wiki/Q7448970","display_name":"SemEval","level":3,"score":0.7313003540039062},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.6976215839385986},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6704211831092834},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.6574059128761292},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6185795068740845},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6022210717201233},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.5662471652030945},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.5282360911369324},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.4920324981212616},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4612506628036499},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.4529784917831421},{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.4219944477081299},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.314656525850296},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.1323142945766449},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.08594244718551636},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","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},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2019/707","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2019/707","pdf_url":"https://www.ijcai.org/proceedings/2019/0707.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.24963/ijcai.2019/707","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2019/707","pdf_url":"https://www.ijcai.org/proceedings/2019/0707.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Quality Education","score":0.6100000143051147,"id":"https://metadata.un.org/sdg/4"}],"awards":[{"id":"https://openalex.org/G3031428753","display_name":null,"funder_award_id":"61772568","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4834643226","display_name":null,"funder_award_id":"18lgzd15","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2964401366.pdf","grobid_xml":"https://content.openalex.org/works/W2964401366.grobid-xml"},"referenced_works_count":28,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1614298861","https://openalex.org/W1930677882","https://openalex.org/W2113125055","https://openalex.org/W2133564696","https://openalex.org/W2250539671","https://openalex.org/W2251124635","https://openalex.org/W2251648804","https://openalex.org/W2252057809","https://openalex.org/W2296071000","https://openalex.org/W2493916176","https://openalex.org/W2529550020","https://openalex.org/W2562607067","https://openalex.org/W2740567223","https://openalex.org/W2757541972","https://openalex.org/W2767439512","https://openalex.org/W2788810909","https://openalex.org/W2789190634","https://openalex.org/W2799009183","https://openalex.org/W2804000041","https://openalex.org/W2898642169","https://openalex.org/W2951008357","https://openalex.org/W2962808042","https://openalex.org/W2963168371","https://openalex.org/W2963240575","https://openalex.org/W2963626623","https://openalex.org/W2964164368","https://openalex.org/W4394643672"],"related_works":["https://openalex.org/W3116116498","https://openalex.org/W3029012650","https://openalex.org/W1988325893","https://openalex.org/W2776212826","https://openalex.org/W2117643817","https://openalex.org/W2968543375","https://openalex.org/W2098784136","https://openalex.org/W4288558800","https://openalex.org/W2953770453","https://openalex.org/W2888625260"],"abstract_inverted_index":{"Aspect":[0],"level":[1,75],"sentiment":[2,8,76,165],"classification":[3],"aims":[4,161],"at":[5],"identifying":[6],"the":[7,30,37,43,49,59,62,95,98,107,119,133,139,147,164,168,176,181,191],"of":[9,94,118,146,167,183],"each":[10],"aspect":[11,27,63,74,96],"term":[12],"in":[13,42,122,132],"a":[14,70,155,173,199],"sentence.":[15],"Deep":[16],"memory":[17,80,104,121],"networks":[18],"often":[19],"use":[20,145],"location":[21,51],"information":[22,39,57,93,101,131],"between":[23],"context":[24,86,120,156],"word":[25,50],"and":[26,48,55,85,97,128,190],"to":[28,162],"generate":[29],"memory.":[31],"Although":[32],"improved":[33],"results":[34,193],"are":[35,126],"achieved,":[36],"relation":[38,100,149],"among":[40],"aspects":[41],"same":[44,134],"sentence":[45,135,170],"is":[46,136],"ignored":[47],"can't":[52],"bring":[53],"enough":[54],"accurate":[56],"for":[58,73,138,171,175],"analysis":[60],"on":[61,112,186],"sentiment.":[64],"In":[65],"this":[66],"paper,":[67],"we":[68,151],"propose":[69],"novel":[71],"framework":[72],"classification,":[77],"deep":[78,103],"mask":[79],"network":[81],"with":[82],"semantic":[83,91,113],"dependency":[84,114],"moment":[87,157],"(DMMN-SDCM),":[88],"which":[89,160],"integrates":[90],"parsing":[92],"inter-aspect":[99,130,148],"into":[102],"network.":[105],"With":[106],"designed":[108],"attention":[109],"mechanism":[110],"based":[111],"information,":[115,150],"different":[116,123],"parts":[117],"computational":[124],"layers":[125],"selected":[127],"useful":[129],"exploited":[137],"desired":[140,177],"aspect.":[141,178],"To":[142],"make":[143],"full":[144],"also":[152],"jointly":[153],"learn":[154,163],"learning":[158],"task,":[159],"distribution":[166],"entire":[169],"providing":[172],"background":[174],"We":[179],"examined":[180],"merit":[182],"our":[184,196],"model":[185,197],"SemEval":[187],"2014":[188],"Datasets,":[189],"experimental":[192],"show":[194],"that":[195],"achieves":[198],"state-of-the-art":[200],"performance.":[201]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":9},{"year":2021,"cited_by_count":15},{"year":2020,"cited_by_count":11}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
