{"id":"https://openalex.org/W4389519316","doi":"https://doi.org/10.18653/v1/2023.findings-emnlp.107","title":"Global Structure Knowledge-Guided Relation Extraction Method for Visually-Rich Document","display_name":"Global Structure Knowledge-Guided Relation Extraction Method for Visually-Rich Document","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4389519316","doi":"https://doi.org/10.18653/v1/2023.findings-emnlp.107"},"language":"en","primary_location":{"id":"doi:10.18653/v1/2023.findings-emnlp.107","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2023.findings-emnlp.107","pdf_url":"https://aclanthology.org/2023.findings-emnlp.107.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":"Findings of the Association for Computational Linguistics: EMNLP 2023","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2023.findings-emnlp.107.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5019840216","display_name":"Xiangnan Chen","orcid":"https://orcid.org/0000-0002-6744-3418"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiangnan Chen","raw_affiliation_strings":["Zhejiang University"],"affiliations":[{"raw_affiliation_string":"Zhejiang University","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026163732","display_name":"Qian Xiao","orcid":"https://orcid.org/0000-0001-7191-414X"},"institutions":[{"id":"https://openalex.org/I4210095624","display_name":"Alibaba Group (United States)","ror":"https://ror.org/00rn0m335","country_code":"US","type":"company","lineage":["https://openalex.org/I4210095624","https://openalex.org/I45928872"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Qian Xiao","raw_affiliation_strings":["Alibaba Group"],"affiliations":[{"raw_affiliation_string":"Alibaba Group","institution_ids":["https://openalex.org/I4210095624"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100648014","display_name":"Juncheng Li","orcid":"https://orcid.org/0000-0001-7314-6754"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Juncheng Li","raw_affiliation_strings":["Zhejiang University"],"affiliations":[{"raw_affiliation_string":"Zhejiang University","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112924241","display_name":"Duo Dong","orcid":null},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Duo Dong","raw_affiliation_strings":["Zhejiang University"],"affiliations":[{"raw_affiliation_string":"Zhejiang University","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100580796","display_name":"Jun Lin","orcid":null},"institutions":[{"id":"https://openalex.org/I4210095624","display_name":"Alibaba Group (United States)","ror":"https://ror.org/00rn0m335","country_code":"US","type":"company","lineage":["https://openalex.org/I4210095624","https://openalex.org/I45928872"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jun Lin","raw_affiliation_strings":["Alibaba Group"],"affiliations":[{"raw_affiliation_string":"Alibaba Group","institution_ids":["https://openalex.org/I4210095624"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101985030","display_name":"Xiaozhong Liu","orcid":"https://orcid.org/0000-0003-3477-8323"},"institutions":[{"id":"https://openalex.org/I107077323","display_name":"Worcester Polytechnic Institute","ror":"https://ror.org/05ejpqr48","country_code":"US","type":"education","lineage":["https://openalex.org/I107077323"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaozhong Liu","raw_affiliation_strings":["Worcester Polytechnic Institute {xnchen2020,junchengli,22121222,"],"affiliations":[{"raw_affiliation_string":"Worcester Polytechnic Institute {xnchen2020,junchengli,22121222,","institution_ids":["https://openalex.org/I107077323"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063062444","display_name":"Siliang Tang","orcid":"https://orcid.org/0000-0002-7356-9711"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Siliang Tang","raw_affiliation_strings":["Zhejiang University"],"affiliations":[{"raw_affiliation_string":"Zhejiang University","institution_ids":["https://openalex.org/I76130692"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5019840216"],"corresponding_institution_ids":["https://openalex.org/I76130692"],"apc_list":null,"apc_paid":null,"fwci":0.6852,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.76747091,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1587","last_page":"1598"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9983000159263611,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9983000159263611,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.9969000220298767,"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/pairwise-comparison","display_name":"Pairwise comparison","score":0.7374616265296936},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7067546248435974},{"id":"https://openalex.org/keywords/relationship-extraction","display_name":"Relationship extraction","score":0.6580690741539001},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.6573433876037598},{"id":"https://openalex.org/keywords/resource","display_name":"Resource (disambiguation)","score":0.48609161376953125},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.42948824167251587},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.39476409554481506},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.35065215826034546},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3440208435058594},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3312667906284332}],"concepts":[{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.7374616265296936},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7067546248435974},{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.6580690741539001},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.6573433876037598},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.48609161376953125},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42948824167251587},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.39476409554481506},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.35065215826034546},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3440208435058594},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3312667906284332},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2023.findings-emnlp.107","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2023.findings-emnlp.107","pdf_url":"https://aclanthology.org/2023.findings-emnlp.107.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":"Findings of the Association for Computational Linguistics: EMNLP 2023","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2023.findings-emnlp.107","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2023.findings-emnlp.107","pdf_url":"https://aclanthology.org/2023.findings-emnlp.107.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":"Findings of the Association for Computational Linguistics: EMNLP 2023","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1781607827","display_name":null,"funder_award_id":"62272411","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3551653136","display_name":null,"funder_award_id":"2018AAA0101900","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G5087339601","display_name":null,"funder_award_id":"01019","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320318398","display_name":"Ant Group","ror":null},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322927","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4389519316.pdf"},"referenced_works_count":40,"referenced_works":["https://openalex.org/W2908510526","https://openalex.org/W2945260553","https://openalex.org/W2965897997","https://openalex.org/W2986619406","https://openalex.org/W3015468748","https://openalex.org/W3034549794","https://openalex.org/W3035390927","https://openalex.org/W3104953317","https://openalex.org/W3138516171","https://openalex.org/W3152774549","https://openalex.org/W3171975879","https://openalex.org/W3173306993","https://openalex.org/W3173325518","https://openalex.org/W3183719433","https://openalex.org/W3190448953","https://openalex.org/W3205949070","https://openalex.org/W3207806388","https://openalex.org/W3212748493","https://openalex.org/W3213783001","https://openalex.org/W3214661002","https://openalex.org/W4221167941","https://openalex.org/W4226020328","https://openalex.org/W4285428168","https://openalex.org/W4287183331","https://openalex.org/W4287667694","https://openalex.org/W4290058858","https://openalex.org/W4295312788","https://openalex.org/W4304013646","https://openalex.org/W4304080189","https://openalex.org/W4312245888","https://openalex.org/W4312812783","https://openalex.org/W4324321325","https://openalex.org/W4366850747","https://openalex.org/W4375928968","https://openalex.org/W4376312115","https://openalex.org/W4376653374","https://openalex.org/W4380994594","https://openalex.org/W4382132560","https://openalex.org/W4385574075","https://openalex.org/W4385714029"],"related_works":["https://openalex.org/W2976808399","https://openalex.org/W2609844752","https://openalex.org/W2981341912","https://openalex.org/W4285246823","https://openalex.org/W4226278302","https://openalex.org/W4385734297","https://openalex.org/W4221160509","https://openalex.org/W2547211086","https://openalex.org/W2538200646","https://openalex.org/W2888033806"],"abstract_inverted_index":{"Visual":[0],"Relation":[1],"Extraction":[2,72],"(VRE)":[3],"is":[4,97,117],"a":[5,67,87],"powerful":[6],"means":[7],"of":[8,43,90,111],"discovering":[9],"relationships":[10],"between":[11],"entities":[12],"within":[13],"visually-rich":[14],"documents.":[15],"Existing":[16],"methods":[17,141],"often":[18],"focus":[19],"on":[20,82],"manipulating":[21],"entity":[22,38,83,122],"features":[23],"to":[24,52,128],"find":[25],"pairwise":[26],"relations,":[27],"yet":[28],"neglect":[29],"the":[30,49,91,100,109,112,143,161],"more":[31],"fundamental":[32],"structural":[33,95],"information":[34,46],"that":[35,135],"links":[36],"disparate":[37],"pairs":[39,84],"together.":[40],"The":[41],"absence":[42],"global":[44,94,125],"structure":[45,126],"may":[47],"make":[48],"model":[50],"struggle":[51],"learn":[53],"long-range":[54],"relations":[55],"and":[56,124],"easily":[57],"predict":[58],"conflicted":[59],"results.":[60],"To":[61],"alleviate":[62],"such":[63],"limitations,":[64],"we":[65],"propose":[66],"GlObal":[68],"Structure":[69],"knowledge-guided":[70],"relation":[71,80],"(GOSE)":[73],"framework.":[74],"GOSE":[75,136],"initiates":[76],"by":[77],"generating":[78],"preliminary":[79],"predictions":[81],"extracted":[85],"from":[86,99],"scanned":[88],"image":[89],"document.":[92],"Subsequently,":[93],"knowledge":[96,127],"captured":[98],"preceding":[101],"iterative":[102],"predictions,":[103],"which":[104],"are":[105],"then":[106],"incorporated":[107],"into":[108],"representations":[110,123],"entities.":[113],"This":[114],"\u201cgenerate-capture-incorporate\u201d":[115],"cycle":[116],"repeated":[118],"multiple":[119],"times,":[120],"allowing":[121],"be":[129],"mutually":[130],"reinforced.":[131],"Extensive":[132],"experiments":[133],"validate":[134],"not":[137],"only":[138],"outperforms":[139],"existing":[140],"in":[142,160],"standard":[144],"fine-tuning":[145],"setting":[146],"but":[147],"also":[148],"reveals":[149],"superior":[150],"cross-lingual":[151],"learning":[152],"capabilities;":[153],"indeed,":[154],"even":[155],"yields":[156],"stronger":[157],"data-efficient":[158],"performance":[159],"low-resource":[162],"setting.":[163]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3}],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-10-10T00:00:00"}
