{"id":"https://openalex.org/W3111707478","doi":"https://doi.org/10.1145/3421515.3421536","title":"Joint Opinion Target and Target-oriented Opinion Words Extraction by BERT and IOT Model","display_name":"Joint Opinion Target and Target-oriented Opinion Words Extraction by BERT and IOT Model","publication_year":2020,"publication_date":"2020-07-11","ids":{"openalex":"https://openalex.org/W3111707478","doi":"https://doi.org/10.1145/3421515.3421536","mag":"3111707478"},"language":"en","primary_location":{"id":"doi:10.1145/3421515.3421536","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3421515.3421536","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 2nd Symposium on Signal Processing Systems","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/A5036684093","display_name":"Yuanfa Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yuanfa Zhu","raw_affiliation_strings":["Guangdong University of Technology, China"],"affiliations":[{"raw_affiliation_string":"Guangdong University of Technology, China","institution_ids":["https://openalex.org/I139024713"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062290117","display_name":"Depei Wang","orcid":"https://orcid.org/0000-0001-6972-5876"},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Depei Wang","raw_affiliation_strings":["Guangdong University of Technology, China"],"affiliations":[{"raw_affiliation_string":"Guangdong University of Technology, China","institution_ids":["https://openalex.org/I139024713"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101643168","display_name":"Weiwen Zhang","orcid":"https://orcid.org/0000-0002-5098-6459"},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weiwen Zhang","raw_affiliation_strings":["Guangdong University of Technology, China"],"affiliations":[{"raw_affiliation_string":"Guangdong University of Technology, China","institution_ids":["https://openalex.org/I139024713"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5036684093"],"corresponding_institution_ids":["https://openalex.org/I139024713"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.1672861,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"120","last_page":"125"},"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":0.9976999759674072,"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":0.9958000183105469,"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":0.8327864408493042},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.8250563144683838},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.665993332862854},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.6015400886535645},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5546422004699707},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.5373465418815613},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5306362509727478},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.47429850697517395},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.06589093804359436}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8327864408493042},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.8250563144683838},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.665993332862854},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.6015400886535645},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5546422004699707},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.5373465418815613},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5306362509727478},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.47429850697517395},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.06589093804359436},{"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/C170154142","wikidata":"https://www.wikidata.org/wiki/Q150737","display_name":"Architectural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3421515.3421536","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3421515.3421536","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 2nd Symposium on Signal Processing Systems","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W2044429219","https://openalex.org/W2064675550","https://openalex.org/W2134033474","https://openalex.org/W2251124635","https://openalex.org/W2251294039","https://openalex.org/W2251648804","https://openalex.org/W2269911130","https://openalex.org/W2465041517","https://openalex.org/W2465978385","https://openalex.org/W2612690371","https://openalex.org/W2756816896","https://openalex.org/W2759083144","https://openalex.org/W2889191048","https://openalex.org/W2890240222","https://openalex.org/W2892181857","https://openalex.org/W2946015932","https://openalex.org/W2952620744","https://openalex.org/W2965510113","https://openalex.org/W2970895602","https://openalex.org/W2985056549"],"related_works":["https://openalex.org/W2548633793","https://openalex.org/W3013279174","https://openalex.org/W2941935829","https://openalex.org/W2596247554","https://openalex.org/W4301373556","https://openalex.org/W3132372214","https://openalex.org/W4224284088","https://openalex.org/W4286571989","https://openalex.org/W2765903680","https://openalex.org/W4317653575"],"abstract_inverted_index":{"In":[0,59],"this":[1],"paper,":[2],"we":[3,30,68,96],"investigate":[4],"two":[5],"sub-tasks":[6],"of":[7,40,93,113,139],"aspect-based":[8],"sentiment":[9],"analysis":[10],"(ABSA)":[11],"through":[12],"the":[13,36,52,63,85,91,111,120,125,133],"pre-trained":[14],"language":[15],"model":[16,39,100,108,131],"BERT,":[17],"namely":[18],"opinion":[19,25,41,45,53,57,114,117],"target":[20,42,54,115],"extraction":[21,27,38],"(OTE)":[22],"and":[23,43,55,116],"target-oriented":[24,44],"words":[26,46,118],"(TOWE).":[28],"Specifically,":[29],"build":[31],"a":[32,98],"novel":[33],"framework":[34],"for":[35,101],"joint":[37,130],"feedback,":[47],"which":[48,75],"aims":[49],"to":[50,61,135],"extract":[51,110],"corresponding":[56],"words.":[58],"order":[60],"accomplish":[62],"TOWE":[64],"task":[65],"more":[66,122],"effectively,":[67],"proposed":[69],"an":[70],"IO-LSTM+Transformer":[71],"structure,":[72],"termed":[73],"IOT,":[74],"has":[76,132],"excellent":[77],"performance":[78],"in":[79],"domain-specific":[80],"datasets":[81],"when":[82],"combined":[83],"with":[84],"BERT":[86],"pre-training":[87],"model.":[88,127],"To":[89],"validate":[90],"effectiveness":[92],"our":[94,107,129],"model,":[95],"develop":[97],"pipeline":[99,126],"comparison.":[102],"Experiment":[103],"results":[104],"show":[105],"that":[106],"can":[109],"pair":[112],"from":[119],"sentence":[121],"effectively":[123],"than":[124],"Therefore,":[128],"potential":[134],"facilitate":[136],"other":[137],"tasks":[138],"ABSA.":[140]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
