{"id":"https://openalex.org/W4398161357","doi":"https://doi.org/10.1145/3605098.3635894","title":"Causal-Evidence Graph for Causal Relation Classification","display_name":"Causal-Evidence Graph for Causal Relation Classification","publication_year":2024,"publication_date":"2024-04-08","ids":{"openalex":"https://openalex.org/W4398161357","doi":"https://doi.org/10.1145/3605098.3635894"},"language":"en","primary_location":{"id":"doi:10.1145/3605098.3635894","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3605098.3635894","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3605098.3635894","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3605098.3635894","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5043330748","display_name":"Yuni Susanti","orcid":"https://orcid.org/0009-0001-1314-0286"},"institutions":[{"id":"https://openalex.org/I2252096349","display_name":"Fujitsu (Japan)","ror":"https://ror.org/038e2g226","country_code":"JP","type":"company","lineage":["https://openalex.org/I2252096349"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Yuni Susanti","raw_affiliation_strings":["Artificial Intelligence Lab., Fujitsu LTD., Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Artificial Intelligence Lab., Fujitsu LTD., Tokyo, Japan","institution_ids":["https://openalex.org/I2252096349"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004967018","display_name":"Kanji Uchino","orcid":"https://orcid.org/0009-0002-7736-2504"},"institutions":[{"id":"https://openalex.org/I4210094759","display_name":"Fujitsu (United States)","ror":"https://ror.org/0073whr05","country_code":"US","type":"company","lineage":["https://openalex.org/I2252096349","https://openalex.org/I4210094759"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kanji Uchino","raw_affiliation_strings":["Fujitsu Research of America, Inc., California, United States"],"affiliations":[{"raw_affiliation_string":"Fujitsu Research of America, Inc., California, United States","institution_ids":["https://openalex.org/I4210094759"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5043330748"],"corresponding_institution_ids":["https://openalex.org/I2252096349"],"apc_list":null,"apc_paid":null,"fwci":0.4989,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.63674955,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"714","last_page":"722"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9975000023841858,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9861999750137329,"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":0.592515766620636},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.4917059540748596},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.41797012090682983},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.37661147117614746},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.34634870290756226},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.22941920161247253}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.592515766620636},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.4917059540748596},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.41797012090682983},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37661147117614746},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.34634870290756226},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.22941920161247253}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3605098.3635894","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3605098.3635894","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3605098.3635894","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3605098.3635894","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3605098.3635894","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3605098.3635894","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.4099999964237213,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4398161357.pdf"},"referenced_works_count":30,"referenced_works":["https://openalex.org/W1593045043","https://openalex.org/W2052217781","https://openalex.org/W2076581021","https://openalex.org/W2106560678","https://openalex.org/W2108134720","https://openalex.org/W2158419693","https://openalex.org/W2161741964","https://openalex.org/W2166474856","https://openalex.org/W2170189740","https://openalex.org/W2334487059","https://openalex.org/W2488809669","https://openalex.org/W2602331152","https://openalex.org/W2911489562","https://openalex.org/W2912500072","https://openalex.org/W2963341956","https://openalex.org/W2970778761","https://openalex.org/W2971196929","https://openalex.org/W3037109418","https://openalex.org/W3046375318","https://openalex.org/W3115151495","https://openalex.org/W3123829608","https://openalex.org/W3165832785","https://openalex.org/W4205677923","https://openalex.org/W4226191767","https://openalex.org/W4285306300","https://openalex.org/W4302423442","https://openalex.org/W4307935822","https://openalex.org/W4320035179","https://openalex.org/W4385572820","https://openalex.org/W4385647122"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W4234874385","https://openalex.org/W2390279801","https://openalex.org/W2323648130","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2157140558","https://openalex.org/W2376932109","https://openalex.org/W2378782423"],"abstract_inverted_index":{"This":[0],"paper":[1],"aims":[2],"toward":[3],"an":[4,80,84],"enhancement":[5],"for":[6],"automatic":[7],"causal":[8,34,45,114],"relation":[9,46],"classification":[10,47],"from":[11,33,54,64],"text":[12],"sources.":[13],"We":[14,36],"introduce":[15],"a":[16,23,43],"Causal":[17],"Evidence":[18],"Graph":[19],"(CEG),":[20],"which":[21,118],"is":[22],"graph-structured":[24],"representation":[25],"of":[26,29,77,107],"lexical":[27,113],"evidence":[28,115],"causality":[30],"extracted":[31],"automatically":[32],"texts.":[35],"further":[37],"incorporate":[38],"the":[39,51,55,60,96,105,109,125],"CEG":[40,57],"graph":[41,58],"into":[42],"supervised":[44],"model":[48,110],"by":[49,124],"learning":[50],"joint":[52],"representations":[53],"generated":[56],"with":[59,112],"sentence":[61],"encoding":[62],"obtained":[63],"BERT":[65],"pre-trained":[66,127],"language":[67,129],"models.":[68],"Despite":[69],"its":[70],"simplicity,":[71],"extensive":[72],"experiments":[73],"on":[74],"three":[75],"types":[76],"biomedical":[78],"and":[79,90,98],"open-domain":[81],"datasets":[82],"show":[83],"overall":[85],"improvement,":[86],"up":[87],"to":[88],"2.6%":[89],"4.7%":[91],"F1":[92],"score":[93],"improvements":[94],"over":[95],"state-of-the-art":[97],"baseline":[99],"models,":[100],"respectively.":[101],"The":[102],"results":[103],"proved":[104],"effectiveness":[106],"injecting":[108],"directly":[111],"as":[116,132],"features,":[117],"might":[119],"not":[120],"be":[121],"explicitly":[122],"represented":[123],"current":[126],"large":[128],"models":[130],"such":[131],"BERT.":[133]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
