ISIKUN at the FinCausal 2020: Linguistically informed machine-learning approach for causality identification in financial documents

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dc.authorid0000-0002-7097-6143
dc.contributor.authorÖzenir, Hüseyin Gökberken_US
dc.contributor.authorKaradeniz, İlknuren_US
dc.date.accessioned2022-02-14T13:29:16Z
dc.date.available2022-02-14T13:29:16Z
dc.date.issued2020
dc.departmentIşık Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.departmentIşık University, Faculty of Engineering, Department of Computer Engineeringen_US
dc.description.abstractThis paper presents our participation to the FinCausal-2020 Shared Task whose ultimate aim is to extract cause-effect relations from a given financial text. Our participation includes two systems for the two sub-tasks of the FinCausal-2020 Shared Task. The first sub-task (Task-1) consists of the binary classification of the given sentences as causal meaningful (1) or causal meaningless (0). Our approach for the Task-1 includes applying linear support vector machines after transforming the input sentences into vector representations using term frequency-inverse document frequency scheme with 3-grams. The second sub-task (Task-2) consists of the identification of the cause-effect relations in the sentences, which are detected as causal meaningful. Our approach for the Task-2 is a CRF-based model which uses linguistically informed features. For the Task-1, the obtained results show that there is a small difference between the proposed approach based on linear support vector machines (F-score 94%), which requires less time compared to the BERT-based baseline (F-score 95%). For the Task-2, although a minor modifications such as the learning algorithm type and the feature representations are made in the conditional random fields based baseline (F-score 52%), we have obtained better results (F-score 60%). The source codes for the both tasks are available online (https://github.com/ozenirgokberk/FinCausal2020.git/).en_US
dc.description.versionPublisher's Versionen_US
dc.identifier.citationÖzenir, H. G. & Karadeniz, İ. (2020). ISIKUN at the FinCausal 2020: Linguistically informed machine-learning approach for causality identification in financial documents. Paper presented by FNP-FNS 2020 - 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation, Proceedings, 85-89.en_US
dc.identifier.endpage89
dc.identifier.isbn9781952148408
dc.identifier.scopus2-s2.0-85123925184
dc.identifier.scopusN/A
dc.identifier.startpage85
dc.identifier.urihttps://hdl.handle.net/11729/3430
dc.indekslendigikaynakScopusen_US
dc.institutionauthorÖzenir, Hüseyin Gökberken_US
dc.institutionauthorKaradeniz, İlknuren_US
dc.institutionauthorid0000-0002-7097-6143
dc.language.isoenen_US
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.publisherAssociation for Computational Linguistics (ACL)en_US
dc.relation.ispartofFNP-FNS 2020 - 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation, Proceedingsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectClassification (of information)en_US
dc.subjectFins (heat exchange)en_US
dc.subjectLearning algorithmsen_US
dc.subjectLinguisticsen_US
dc.subjectRandom processesen_US
dc.subjectSupport vector machinesen_US
dc.subjectText processingen_US
dc.subjectBinary classificationen_US
dc.subjectCause-effecten_US
dc.subjectF-scoreen_US
dc.subjectFeature representationen_US
dc.subjectLinear Support Vector Machinesen_US
dc.subjectMachine learning approachesen_US
dc.subjectSource codesen_US
dc.subjectSubtasken_US
dc.subjectTerm frequencyinverse document frequency (TF-IDF)en_US
dc.subjectVector representationsen_US
dc.subjectFinanceen_US
dc.titleISIKUN at the FinCausal 2020: Linguistically informed machine-learning approach for causality identification in financial documentsen_US
dc.typeConference Objecten_US
dspace.entity.typePublication

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