{"id":"https://openalex.org/W2962843214","doi":"https://doi.org/10.18653/v1/d16-1059","title":"Recursive Neural Conditional Random Fields for Aspect-based Sentiment Analysis","display_name":"Recursive Neural Conditional Random Fields for Aspect-based Sentiment Analysis","publication_year":2016,"publication_date":"2016-01-01","ids":{"openalex":"https://openalex.org/W2962843214","doi":"https://doi.org/10.18653/v1/d16-1059","mag":"2962843214"},"language":"en","primary_location":{"id":"doi:10.18653/v1/d16-1059","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d16-1059","pdf_url":"https://www.aclweb.org/anthology/D16-1059.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 2016 Conference on Empirical Methods in Natural\n          Language Processing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/D16-1059.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":true,"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":"middle","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":false,"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"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002373633","display_name":"Daniel Dahlmeier","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Daniel Dahlmeier","raw_affiliation_strings":["SAP Innovation Center Singapore"],"affiliations":[{"raw_affiliation_string":"SAP Innovation Center Singapore","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010903591","display_name":"Xiaokui Xiao","orcid":"https://orcid.org/0000-0003-0914-4580"},"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":"Xiaokui Xiao","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":4,"corresponding_author_ids":["https://openalex.org/A5078565848"],"corresponding_institution_ids":["https://openalex.org/I172675005"],"apc_list":null,"apc_paid":null,"fwci":41.08,"has_fulltext":true,"cited_by_count":424,"citation_normalized_percentile":{"value":0.99771757,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"616","last_page":"626"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9998000264167786,"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/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/T10028","display_name":"Topic Modeling","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"}},{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9970999956130981,"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":0.6711338758468628},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.6424515247344971},{"id":"https://openalex.org/keywords/conditional-random-field","display_name":"Conditional random field","score":0.6357400417327881},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5058888792991638},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3810942769050598}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6711338758468628},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.6424515247344971},{"id":"https://openalex.org/C152565575","wikidata":"https://www.wikidata.org/wiki/Q1124538","display_name":"Conditional random field","level":2,"score":0.6357400417327881},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5058888792991638},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3810942769050598}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/d16-1059","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d16-1059","pdf_url":"https://www.aclweb.org/anthology/D16-1059.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 2016 Conference on Empirical Methods in Natural\n          Language Processing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/d16-1059","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d16-1059","pdf_url":"https://www.aclweb.org/anthology/D16-1059.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 2016 Conference on Empirical Methods in Natural\n          Language Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.75,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[{"id":"https://openalex.org/G3034753964","display_name":null,"funder_award_id":"grant","funder_id":"https://openalex.org/F4320320671","funder_display_name":"National Research Foundation"},{"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/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"},{"id":"https://openalex.org/F4320320709","display_name":"National Research Foundation Singapore","ror":"https://ror.org/03cpyc314"},{"id":"https://openalex.org/F4320320766","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2962843214.pdf","grobid_xml":"https://content.openalex.org/works/W2962843214.grobid-xml"},"referenced_works_count":51,"referenced_works":["https://openalex.org/W22861983","https://openalex.org/W71795751","https://openalex.org/W182185074","https://openalex.org/W1026216613","https://openalex.org/W1423339008","https://openalex.org/W1515087027","https://openalex.org/W1517771839","https://openalex.org/W1581485226","https://openalex.org/W1614298861","https://openalex.org/W1832693441","https://openalex.org/W1859957297","https://openalex.org/W1889268436","https://openalex.org/W1991819034","https://openalex.org/W1996430422","https://openalex.org/W2001259128","https://openalex.org/W2019207508","https://openalex.org/W2027731328","https://openalex.org/W2049493498","https://openalex.org/W2064675550","https://openalex.org/W2081375810","https://openalex.org/W2096141509","https://openalex.org/W2097606805","https://openalex.org/W2097726431","https://openalex.org/W2100362224","https://openalex.org/W2104518905","https://openalex.org/W2112744748","https://openalex.org/W2117499988","https://openalex.org/W2120615054","https://openalex.org/W2125247664","https://openalex.org/W2130237711","https://openalex.org/W2131305515","https://openalex.org/W2131744502","https://openalex.org/W2137207778","https://openalex.org/W2144012961","https://openalex.org/W2147880316","https://openalex.org/W2149557440","https://openalex.org/W2154970197","https://openalex.org/W2159457224","https://openalex.org/W2160660844","https://openalex.org/W2161222299","https://openalex.org/W2161672051","https://openalex.org/W2251124635","https://openalex.org/W2251648804","https://openalex.org/W2251939518","https://openalex.org/W2252024663","https://openalex.org/W2269911130","https://openalex.org/W2328290429","https://openalex.org/W2963556938","https://openalex.org/W3049711450","https://openalex.org/W4248506559","https://openalex.org/W4299026567"],"related_works":["https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2356597680","https://openalex.org/W2114846443","https://openalex.org/W2093471820","https://openalex.org/W3102147106","https://openalex.org/W2347460059","https://openalex.org/W50079190","https://openalex.org/W2886890203","https://openalex.org/W3192589309"],"abstract_inverted_index":{"In":[0,40],"aspect-based":[1],"sentiment":[2],"analysis,":[3],"extracting":[4],"aspect":[5,31,64,81],"terms":[6,34,67],"along":[7],"with":[8],"the":[9,19,95,108,117,130,134],"opinions":[10],"being":[11],"expressed":[12],"from":[13,110],"user-generated":[14],"content":[15],"is":[16,35,88],"one":[17],"of":[18,119,133],"most":[20],"important":[21],"subtasks.":[22],"Previous":[23],"studies":[24],"have":[25],"shown":[26],"that":[27,49],"exploiting":[28],"connections":[29],"between":[30,80],"and":[32,54,65,76,82],"opinion":[33,66,83],"promising":[36],"for":[37,62],"this":[38,41],"task.":[39],"paper,":[42],"we":[43],"propose":[44],"a":[45,59],"novel":[46],"joint":[47],"model":[48,71,97,122],"integrates":[50],"recursive":[51],"neural":[52],"networks":[53],"conditional":[55],"random":[56],"fields":[57],"into":[58,94],"unified":[60],"framework":[61],"explicit":[63],"co-extraction.":[68],"The":[69],"proposed":[70,96,121],"learns":[72],"high-level":[73],"discriminative":[74],"features":[75,93],"double":[77],"propagates":[78],"information":[79,102],"terms,":[84],"simultaneously.":[85],"Moreover,":[86],"it":[87],"flexible":[89],"to":[90,98],"incorporate":[91],"hand-crafted":[92],"further":[99],"boost":[100],"its":[101],"extraction":[103],"performance.":[104],"Experimental":[105],"results":[106],"on":[107],"dataset":[109],"SemEval":[111],"Challenge":[112],"2014":[113],"task":[114],"4":[115],"show":[116],"superiority":[118],"our":[120],"over":[123],"several":[124],"baseline":[125],"methods":[126],"as":[127,129],"well":[128],"winning":[131],"systems":[132],"challenge.":[135]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":24},{"year":2024,"cited_by_count":23},{"year":2023,"cited_by_count":46},{"year":2022,"cited_by_count":70},{"year":2021,"cited_by_count":64},{"year":2020,"cited_by_count":88},{"year":2019,"cited_by_count":60},{"year":2018,"cited_by_count":22},{"year":2017,"cited_by_count":11},{"year":2015,"cited_by_count":12}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}