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.

本文提出了一种从双语文本数据中提取金融因果知识的方法。特定领域的因果知识在人类智力活动，特别是专家决策中发挥着重要作用。特别是在金融领域，基金经理、金融分析师等在工作中需要因果知识。自然语言处理对于提取人类感知的因果关系非常有效；然而，现有方法存在两个主要问题。首先，必须从多种语言的文本数据中提取与全球活动相关的因果关系；然而，迄今为止，多语言因果关系提取尚未建立。其次，提取复杂因果结构（例如嵌套因果关系）的技术还不够。我们认为使用通用依赖关系的模型可以提取双语，并且可以使用线索建立嵌套因果关系，例如“因为”和“因为”。因此，为了解决这些问题，所提出的模型根据多语言文本数据中的此类线索和普遍依赖性来提取嵌套因果关系。所提出的金融因果关系提取方法在金融领域的双语文本数据上进行了评估，结果表明所提出的模型在实验中优于现有模型。