{"id":"https://openalex.org/W4327644080","doi":"https://doi.org/10.1007/978-3-031-28244-7_28","title":"Temporal Natural Language Inference: Evidence-Based Evaluation of\u00a0Temporal Text Validity","display_name":"Temporal Natural Language Inference: Evidence-Based Evaluation of\u00a0Temporal Text Validity","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4327644080","doi":"https://doi.org/10.1007/978-3-031-28244-7_28"},"language":"en","primary_location":{"id":"doi:10.1007/978-3-031-28244-7_28","is_oa":true,"landing_page_url":"https://doi.org/10.1007/978-3-031-28244-7_28","pdf_url":null,"source":{"id":"https://openalex.org/S106296714","display_name":"Lecture notes in computer science","issn_l":"0302-9743","issn":["0302-9743","1611-3349"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"book series"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Lecture Notes in Computer Science","raw_type":"book-chapter"},"type":"book-chapter","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1007/978-3-031-28244-7_28","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5018169173","display_name":"Taishi Hosokawa","orcid":null},"institutions":[{"id":"https://openalex.org/I22299242","display_name":"Kyoto University","ror":"https://ror.org/02kpeqv85","country_code":"JP","type":"education","lineage":["https://openalex.org/I22299242"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Taishi Hosokawa","raw_affiliation_strings":["Kyoto University, Kyoto, Japan"],"affiliations":[{"raw_affiliation_string":"Kyoto University, Kyoto, Japan","institution_ids":["https://openalex.org/I22299242"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079733597","display_name":"Adam Jatowt","orcid":"https://orcid.org/0000-0001-7235-0665"},"institutions":[{"id":"https://openalex.org/I190249584","display_name":"Universit\u00e4t Innsbruck","ror":"https://ror.org/054pv6659","country_code":"AT","type":"education","lineage":["https://openalex.org/I190249584"]}],"countries":["AT"],"is_corresponding":true,"raw_author_name":"Adam Jatowt","raw_affiliation_strings":["University of Innsbruck, Innsbruck, Austria"],"affiliations":[{"raw_affiliation_string":"University of Innsbruck, Innsbruck, Austria","institution_ids":["https://openalex.org/I190249584"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075581979","display_name":"Kazunari Sugiyama","orcid":"https://orcid.org/0000-0003-3962-821X"},"institutions":[{"id":"https://openalex.org/I4210105506","display_name":"Osaka Seikei University","ror":"https://ror.org/00yydx071","country_code":"JP","type":"education","lineage":["https://openalex.org/I4210105506"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kazunari Sugiyama","raw_affiliation_strings":["Osaka Seikei University, Osaka, Japan"],"affiliations":[{"raw_affiliation_string":"Osaka Seikei University, Osaka, Japan","institution_ids":["https://openalex.org/I4210105506"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5079733597"],"corresponding_institution_ids":["https://openalex.org/I190249584"],"apc_list":{"value":5000,"currency":"EUR","value_usd":5392},"apc_paid":{"value":5000,"currency":"EUR","value_usd":5392},"fwci":2.6096,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.90072735,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"441","last_page":"458"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":1.0,"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":1.0,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9993000030517578,"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":0.9958999752998352,"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":0.8813861012458801},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6829510927200317},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.6689691543579102},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6455374956130981},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.5617492198944092},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5405402183532715},{"id":"https://openalex.org/keywords/knowledge-base","display_name":"Knowledge base","score":0.5285868644714355},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.4847085475921631},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.4806434214115143}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8813861012458801},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6829510927200317},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6689691543579102},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6455374956130981},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.5617492198944092},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5405402183532715},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.5285868644714355},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.4847085475921631},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.4806434214115143},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/978-3-031-28244-7_28","is_oa":true,"landing_page_url":"https://doi.org/10.1007/978-3-031-28244-7_28","pdf_url":null,"source":{"id":"https://openalex.org/S106296714","display_name":"Lecture notes in computer science","issn_l":"0302-9743","issn":["0302-9743","1611-3349"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"book series"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Lecture Notes in Computer Science","raw_type":"book-chapter"}],"best_oa_location":{"id":"doi:10.1007/978-3-031-28244-7_28","is_oa":true,"landing_page_url":"https://doi.org/10.1007/978-3-031-28244-7_28","pdf_url":null,"source":{"id":"https://openalex.org/S106296714","display_name":"Lecture notes in computer science","issn_l":"0302-9743","issn":["0302-9743","1611-3349"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"book series"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Lecture Notes in Computer Science","raw_type":"book-chapter"},"sustainable_development_goals":[{"display_name":"Quality Education","score":0.7900000214576721,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":59,"referenced_works":["https://openalex.org/W1504636771","https://openalex.org/W1555967944","https://openalex.org/W1840435438","https://openalex.org/W1897507002","https://openalex.org/W2016089260","https://openalex.org/W2057822527","https://openalex.org/W2080133951","https://openalex.org/W2118689949","https://openalex.org/W2130158090","https://openalex.org/W2141539902","https://openalex.org/W2145959026","https://openalex.org/W2185175083","https://openalex.org/W2250539671","https://openalex.org/W2283196293","https://openalex.org/W2466175319","https://openalex.org/W2561529111","https://openalex.org/W2608787653","https://openalex.org/W2739896562","https://openalex.org/W2788496822","https://openalex.org/W2797605755","https://openalex.org/W2798370388","https://openalex.org/W2798665661","https://openalex.org/W2868921360","https://openalex.org/W2898852996","https://openalex.org/W2902075181","https://openalex.org/W2923014074","https://openalex.org/W2931560958","https://openalex.org/W2950339735","https://openalex.org/W2953356739","https://openalex.org/W2962781380","https://openalex.org/W2963101081","https://openalex.org/W2963797084","https://openalex.org/W2963829073","https://openalex.org/W2963846996","https://openalex.org/W2963895422","https://openalex.org/W2963918774","https://openalex.org/W2964263366","https://openalex.org/W2970641574","https://openalex.org/W2970971581","https://openalex.org/W2970986510","https://openalex.org/W2971236147","https://openalex.org/W2983995706","https://openalex.org/W2984008963","https://openalex.org/W2991358176","https://openalex.org/W2998231212","https://openalex.org/W3034602344","https://openalex.org/W3035290244","https://openalex.org/W3080427797","https://openalex.org/W3099023595","https://openalex.org/W3104036557","https://openalex.org/W3172335055","https://openalex.org/W3174464510","https://openalex.org/W3211116231","https://openalex.org/W4230872509","https://openalex.org/W4239647775","https://openalex.org/W4300573994","https://openalex.org/W6600079452","https://openalex.org/W6602074650","https://openalex.org/W6761372199"],"related_works":["https://openalex.org/W3134247745","https://openalex.org/W4226243593","https://openalex.org/W3172691639","https://openalex.org/W2963582704","https://openalex.org/W3157284875","https://openalex.org/W2147241511","https://openalex.org/W2259406085","https://openalex.org/W2099715052","https://openalex.org/W4226247999","https://openalex.org/W3090872036"],"abstract_inverted_index":{"It":[0,30],"is":[1,31,44,90],"important":[2],"to":[3,34,69],"learn":[4],"whether":[5,83,97],"text":[6,75],"information":[7,18,130,156],"remains":[8,101],"valid":[9],"or":[10,93],"not":[11],"for":[12,47,113,128],"various":[13],"applications":[14],"including":[15],"story":[16],"comprehension,":[17],"retrieval,":[19],"and":[20,26,81,116,161],"user":[21],"state":[22],"tracking":[23],"on":[24,139],"microblogs":[25],"via":[27],"chatbot":[28],"conversations.":[29],"also":[32],"beneficial":[33],"deeply":[35],"understand":[36],"the":[37,71,98,155,158,170],"story.":[38],"However,":[39],"this":[40,114],"kind":[41],"of":[42,74],"inference":[43,80],"still":[45,91,100],"difficult":[46],"computers":[48],"as":[49],"it":[50],"requires":[51,79],"temporal":[52,72,140],"commonsense.":[53],"We":[54,107],"propose":[55,124],"a":[56,88,148],"novel":[57],"task,":[58],"Temporal":[59],"Natural":[60],"Language":[61],"Inference,":[62],"inspired":[63],"by":[64],"traditional":[65],"natural":[66],"language":[67],"reasoning":[68],"determine":[70],"validity":[73],"content.":[76,106],"The":[77],"task":[78,115],"judgment":[82],"an":[84,125,132],"action":[85],"expressed":[86],"in":[87,169],"sentence":[89,99],"ongoing":[92],"rather":[94],"completed,":[95],"hence,":[96],"valid,":[102],"given":[103],"its":[104],"supplementary":[105],"first":[108],"construct":[109],"our":[110,164],"own":[111],"dataset":[112],"train":[117],"several":[118],"machine":[119,150],"learning":[120,129,151],"models.":[121],"Then":[122],"we":[123,146],"effective":[126],"method":[127],"from":[131,157],"external":[133],"knowledge":[134,159],"base":[135,160],"that":[136,153,163],"gives":[137],"hints":[138],"commonsense":[141],"knowledge.":[142],"Using":[143],"prepared":[144],"dataset,":[145],"introduce":[147],"new":[149],"model":[152,165],"incorporates":[154],"demonstrate":[162],"outperforms":[166],"state-of-the-art":[167],"approaches":[168],"proposed":[171],"task.":[172]},"counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2}],"updated_date":"2026-01-23T23:20:30.427331","created_date":"2025-10-10T00:00:00"}
