Financial Causality Extraction Based on Universal Dependencies and Clue Expressions
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3AQ55LCB9H" target="_blank" >RIV/00216208:11320/23:Q55LCB9H - isvavai.cz</a>
Result on the web
<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174217910&doi=10.1007%2fs00354-023-00233-2&partnerID=40&md5=e814b7c3e3ac8ca812e190244e8d0479" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174217910&doi=10.1007%2fs00354-023-00233-2&partnerID=40&md5=e814b7c3e3ac8ca812e190244e8d0479</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s00354-023-00233-2" target="_blank" >10.1007/s00354-023-00233-2</a>
Alternative languages
Result language
angličtina
Original language name
Financial Causality Extraction Based on Universal Dependencies and Clue Expressions
Original language description
"This paper proposes a method to extract financial causal knowledge from bi-lingual text data. Domain-specific causal knowledge plays an important role in human intellectual activities, especially expert decision making. Especially, in the financial area, fund managers, financial analysts, etc. need causal knowledge for their works. Natural language processing is highly effective for extracting human-perceived causality; however, there are two major problems with existing methods. First, causality relative to global activities must be extracted from text data in multiple languages; however, multilingual causality extraction has not been established to date. Second, technologies to extract complex causal structures, e.g., nested causalities, are insufficient. We consider that a model using universal dependencies can extract bi-lingual and nested causalities can be established using clues, e.g., “because” and “since.” Thus, to solve these problems, the proposed model extracts nested causalities based on such clues and universal dependencies in multilingual text data. The proposed financial causality extraction method was evaluated on bi-lingual text data from the financial domain, and the results demonstrated that the proposed model outperformed existing models in the experiment. © 2023, The Author(s)."
Czech name
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Czech description
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Classification
Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
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Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
"New Generation Computing"
ISSN
0288-3635
e-ISSN
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Volume of the periodical
3496
Issue of the periodical within the volume
2023
Country of publishing house
US - UNITED STATES
Number of pages
19
Pages from-to
839-857
UT code for WoS article
001080422600001
EID of the result in the Scopus database
2-s2.0-85174217910