{"id":"https://openalex.org/W2799186171","doi":"https://doi.org/10.18653/v1/p18-1202","title":"Recursive Neural Structural Correspondence Network for Cross-domain Aspect and Opinion Co-Extraction","display_name":"Recursive Neural Structural Correspondence Network for Cross-domain Aspect and Opinion Co-Extraction","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2799186171","doi":"https://doi.org/10.18653/v1/p18-1202","mag":"2799186171"},"language":"en","primary_location":{"id":"doi:10.18653/v1/p18-1202","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p18-1202","pdf_url":"https://www.aclweb.org/anthology/P18-1202.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/P18-1202.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5078565848","display_name":"Wenya Wang","orcid":"https://orcid.org/0000-0001-5612-7818"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Wenya Wang","raw_affiliation_strings":["Nanyang Technological University, Singapore","SAP Innovation Center Singapore"],"affiliations":[{"raw_affiliation_string":"Nanyang Technological University, Singapore","institution_ids":["https://openalex.org/I172675005"]},{"raw_affiliation_string":"SAP Innovation Center Singapore","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082984558","display_name":"Sinno Jialin Pan","orcid":"https://orcid.org/0000-0001-6565-3836"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":true,"raw_author_name":"Sinno Jialin Pan","raw_affiliation_strings":["Nanyang Technological University, Singapore"],"affiliations":[{"raw_affiliation_string":"Nanyang Technological University, Singapore","institution_ids":["https://openalex.org/I172675005"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5082984558"],"corresponding_institution_ids":["https://openalex.org/I172675005"],"apc_list":null,"apc_paid":null,"fwci":5.7541,"has_fulltext":true,"cited_by_count":85,"citation_normalized_percentile":{"value":0.96759177,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"2171","last_page":"2181"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9998000264167786,"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":0.9972000122070312,"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":0.8285518288612366},{"id":"https://openalex.org/keywords/relationship-extraction","display_name":"Relationship extraction","score":0.7576252222061157},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7239657640457153},{"id":"https://openalex.org/keywords/automatic-summarization","display_name":"Automatic summarization","score":0.7009981274604797},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.639355480670929},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5932440757751465},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5864326357841492},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5370174646377563},{"id":"https://openalex.org/keywords/dependency","display_name":"Dependency (UML)","score":0.5305402874946594},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.515436053276062},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.5135650634765625},{"id":"https://openalex.org/keywords/dependency-grammar","display_name":"Dependency grammar","score":0.5029897093772888},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4966753125190735},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.4635666310787201},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.460128515958786},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.446052610874176},{"id":"https://openalex.org/keywords/information-extraction","display_name":"Information extraction","score":0.349369078874588},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.1834762692451477},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07638794183731079}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8285518288612366},{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.7576252222061157},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7239657640457153},{"id":"https://openalex.org/C170858558","wikidata":"https://www.wikidata.org/wiki/Q1394144","display_name":"Automatic summarization","level":2,"score":0.7009981274604797},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.639355480670929},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5932440757751465},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5864326357841492},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5370174646377563},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.5305402874946594},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.515436053276062},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.5135650634765625},{"id":"https://openalex.org/C164883195","wikidata":"https://www.wikidata.org/wiki/Q674834","display_name":"Dependency grammar","level":3,"score":0.5029897093772888},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4966753125190735},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.4635666310787201},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.460128515958786},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.446052610874176},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.349369078874588},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.1834762692451477},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07638794183731079},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/p18-1202","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p18-1202","pdf_url":"https://www.aclweb.org/anthology/P18-1202.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/p18-1202","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p18-1202","pdf_url":"https://www.aclweb.org/anthology/P18-1202.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.47999998927116394,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[{"id":"https://openalex.org/G5280407516","display_name":null,"funder_award_id":"Tier-1 grant","funder_id":"https://openalex.org/F4320320766","funder_display_name":"Nanyang Technological University"},{"id":"https://openalex.org/G8771026661","display_name":null,"funder_award_id":"M4081532.020","funder_id":"https://openalex.org/F4320320766","funder_display_name":"Nanyang Technological University"}],"funders":[{"id":"https://openalex.org/F4320320766","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302"},{"id":"https://openalex.org/F4320322724","display_name":"Ministry of Education, India","ror":"https://ror.org/048xjjh50"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2799186171.pdf","grobid_xml":"https://content.openalex.org/works/W2799186171.grobid-xml"},"referenced_works_count":35,"referenced_works":["https://openalex.org/W22861983","https://openalex.org/W1517771839","https://openalex.org/W1614298861","https://openalex.org/W1703030490","https://openalex.org/W1859957297","https://openalex.org/W2027731328","https://openalex.org/W2094061585","https://openalex.org/W2096110600","https://openalex.org/W2096873754","https://openalex.org/W2097606805","https://openalex.org/W2100362224","https://openalex.org/W2121056381","https://openalex.org/W2122838776","https://openalex.org/W2125247664","https://openalex.org/W2130903752","https://openalex.org/W2145071407","https://openalex.org/W2148966043","https://openalex.org/W2153353890","https://openalex.org/W2158108973","https://openalex.org/W2159457224","https://openalex.org/W2160660844","https://openalex.org/W2163302275","https://openalex.org/W2251294039","https://openalex.org/W2251648804","https://openalex.org/W2252007242","https://openalex.org/W2252024663","https://openalex.org/W2515059321","https://openalex.org/W2567698949","https://openalex.org/W2604356472","https://openalex.org/W2962762541","https://openalex.org/W2962843214","https://openalex.org/W2962887999","https://openalex.org/W2963556938","https://openalex.org/W2964344823","https://openalex.org/W4299026567"],"related_works":["https://openalex.org/W2994720652","https://openalex.org/W2003578783","https://openalex.org/W842810586","https://openalex.org/W4319940250","https://openalex.org/W2352298027","https://openalex.org/W2092919065","https://openalex.org/W4236762297","https://openalex.org/W3138801416","https://openalex.org/W2987616634","https://openalex.org/W2369351710"],"abstract_inverted_index":{"Fine-grained":[0],"opinion":[1,8,14],"analysis":[2],"aims":[3],"to":[4,21,53,60,97,106],"extract":[5],"aspect":[6],"and":[7,101],"terms":[9],"from":[10,56],"each":[11],"sentence":[12],"for":[13,24],"summarization.":[15],"Supervised":[16],"learning":[17,38],"methods":[18,50],"have":[19],"proven":[20],"be":[22],"effective":[23],"this":[25,45,66],"task.":[26],"However,":[27],"in":[28,81,115],"many":[29],"domains,":[30],"the":[31,37,57,108,116,120],"lack":[32],"of":[33,39],"labeled":[34],"data":[35],"hinders":[36],"a":[40,70],"precise":[41],"extraction":[42],"model.":[43],"In":[44,65,119],"case,":[46],"unsupervised":[47],"domain":[48,59,78],"adaptation":[49],"are":[51],"desired":[52],"transfer":[54],"knowledge":[55],"source":[58],"any":[61,111],"unlabeled":[62],"target":[63],"domain.":[64],"paper,":[67],"we":[68,122],"develop":[69],"novel":[71],"recursive":[72],"neural":[73],"network":[74],"that":[75],"could":[76],"reduce":[77],"shift":[79],"effectively":[80],"word":[82],"level":[83],"through":[84],"syntactic":[85],"relations.":[86],"We":[87],"treat":[88],"these":[89],"relations":[90],"as":[91],"invariant":[92],"\"pivot":[93],"information\"":[94],"across":[95],"domains":[96],"build":[98],"structural":[99],"correspondences":[100],"generate":[102],"an":[103],"auxiliary":[104],"task":[105],"predict":[107],"relation":[109],"between":[110],"two":[112],"adjacent":[113],"words":[114],"dependency":[117],"tree.":[118],"end,":[121],"demonstrate":[123],"state-ofthe-art":[124],"results":[125],"on":[126],"three":[127],"benchmark":[128],"datasets.":[129]},"counts_by_year":[{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":11},{"year":2023,"cited_by_count":16},{"year":2022,"cited_by_count":15},{"year":2021,"cited_by_count":17},{"year":2020,"cited_by_count":7},{"year":2019,"cited_by_count":9},{"year":2018,"cited_by_count":1}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}