Publication:
The causal news corpus: annotating causal relations in event sentences from news

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dc.contributor.coauthorTan, Fiona Anting
dc.contributor.coauthorCaselli, Tommaso
dc.contributor.coauthorOostdijk, Nelleke
dc.contributor.coauthorNomoto, Tadashi
dc.contributor.coauthorHettiarachchi, Hansi
dc.contributor.coauthorAmeer, Iqra
dc.contributor.coauthorUca, Onur
dc.contributor.coauthorLiza, Farhana Ferdousi
dc.contributor.coauthorHu, Tiancheng
dc.contributor.departmentDepartment of Sociology
dc.contributor.kuauthorHürriyetoğlu, Ali
dc.contributor.kuprofileTeaching Faculty
dc.contributor.otherDepartment of Sociology
dc.contributor.schoolcollegeinstituteCollege of Social Sciences and Humanities
dc.contributor.yokidN/A
dc.date.accessioned2024-11-10T00:01:03Z
dc.date.issued2022
dc.description.abstractDespite the importanceofunderstandingcausality, corporaaddressingcausal relationsare limited. There isadiscrepancy betweenexistingannotationguidelinesofeventcausalityandconventionalcausalitycorporathat focusmoreonlinguistics. Manyguidelinesrestrict themselvestoincludeonlyexplicit relationsorclause-basedarguments. Therefore,weproposean annotationschemaforeventcausalitythataddressestheseconcerns.Weannotated3,559eventsentencesfromprotestevent newswithlabelsonwhether itcontainscausal relationsornot. OurcorpusisknownastheCausalNewsCorpus(CNC).A neuralnetworkbuiltuponastate-of-the-artpre-trainedlanguagemodelperformedwellwith81.20%F1scoreontest set, and83.46%in5-foldscross-validation. CNCistransferableacrosstwoexternalcorpora:CausalTimeBank(CTB)andPenn DiscourseTreebank(PDTB).Leveragingeachoftheseexternaldatasetsfortraining,weachieveduptoapproximately64%F1 ontheCNCtestsetwithoutadditionalfine-tuning. CNCalsoservedasaneffectivetrainingandpre-trainingdataset for the twoexternalcorpora. Lastly,wedemonstratethedifficultyofourtasktothelaymaninacrowd-sourcedannotationexercise. Ourannotatedcorpusispubliclyavailable,providingavaluableresourceforcausaltextminingresearchers.
dc.description.indexedbyWoS
dc.description.indexedbyScopus
dc.description.openaccessNO
dc.description.publisherscopeInternational
dc.description.sponsoredbyTubitakEuN/A
dc.identifier.doiN/A
dc.identifier.isbn979-10-95546-72-6
dc.identifier.quartileN/A
dc.identifier.scopus2-s2.0-85136573341
dc.identifier.urihttps://hdl.handle.net/20.500.14288/15905
dc.identifier.wos889371702043
dc.keywordsCausality
dc.keywordsEvent causality
dc.keywordsText mining
dc.keywordsNatural language understanding
dc.languageEnglish
dc.publisherEUROPEAN LANGUAGE RESOURCES ASSOC-ELRA
dc.relation.grantnoNational Research Foundation, Singapore under its Industry Alignment Fund - Pre-positioning (IAF-PP) Funding Initiative This project is supported by the National Research Foundation, Singapore under its Industry Alignment Fund - Pre-positioning (IAF-PP) Funding Initiative. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore.
dc.sourceLREC 2022: Thirteen International Conference on Language Resources and Evaluation
dc.subjectComputer Science
dc.subjectInterdisciplinary applications
dc.subjectLinguistics
dc.titleThe causal news corpus: annotating causal relations in event sentences from news
dc.typeConference proceeding
dspace.entity.typePublication
local.contributor.authorid0000-0003-3003-1783
local.contributor.kuauthorHürriyetoğlu, Ali
relation.isOrgUnitOfPublication10f5be47-fab1-42a1-af66-1642ba4aff8e
relation.isOrgUnitOfPublication.latestForDiscovery10f5be47-fab1-42a1-af66-1642ba4aff8e

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