ISIKUN at the FinCausal 2020: Linguistically informed machine-learning approach for causality identification in financial documents
Yükleniyor...
Dosyalar
Tarih
2020
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Association for Computational Linguistics (ACL)
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
This 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/).
Açıklama
Anahtar Kelimeler
Classification (of information), Fins (heat exchange), Learning algorithms, Linguistics, Random processes, Support vector machines, Text processing, Binary classification, Cause-effect, F-score, Feature representation, Linear Support Vector Machines, Machine learning approaches, Source codes, Subtask, Term frequencyinverse document frequency (TF-IDF), Vector representations, Finance
Kaynak
FNP-FNS 2020 - 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation, Proceedings
WoS Q Değeri
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Sayı
Künye
Özenir, 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.
