Financial Causality Extraction Based on Universal Dependencies and Clue Expressions
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Published:2023-10-13
Issue:4
Volume:41
Page:839-857
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ISSN:0288-3635
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Container-title:New Generation Computing
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language:en
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Short-container-title:New Gener. Comput.
Author:
Sakaji HirokiORCID, Izumi Kiyoshi
Abstract
AbstractThis 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.
Funder
JSPS KAKENHI JST-Mirai Program JST-PRESTO The University of Tokyo
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Theoretical Computer Science,Software
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