Financial Causality Extraction Based on Universal Dependencies and Clue Expressions

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Financial Causality Extraction Based on Universal Dependencies and Clue Expressions

Popis výsledku v původním jazyce

"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)."

Název v anglickém jazyce

Financial Causality Extraction Based on Universal Dependencies and Clue Expressions

Popis výsledku anglicky

"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)."

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

—

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

"New Generation Computing"

ISSN

0288-3635

e-ISSN

—

Svazek periodika

3496

Číslo periodika v rámci svazku

2023

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

19

Strana od-do

839-857

Kód UT WoS článku

001080422600001

EID výsledku v databázi Scopus

2-s2.0-85174217910