从文本中学习因果贝叶斯网络
Learning Causal Bayesian Networks from Text
摘要：
因果关系构成了人工智能系统中推理和决策的基础。为了利用今天可用的大量文本数据，从文本中自动发现因果关系近年来已成为一个重大挑战。现有的方法在这一领域是有限的，以提取低层次的个别事件之间的关系。为了克服现有方法的局限性，在本文中，我们提出了一种方法自动推理的因果关系，从人类书面语言的概念层次。为此，我们利用从文本中创建的概念和语言变量的层次结构的特点，并表示提取的因果关系的形式的因果贝叶斯网络。我们的实验表明，我们的方法优于现有的方法在推断复杂的因果推理的文本。
英文摘要：
Causal relationships form the basis for reasoning and decision-making in Artificial Intelligence systems. To exploit the large volume of textual data available today, the automatic discovery of causal relationships from text has emerged as a significant challenge in recent years. Existing approaches in this realm are limited to the extraction of low-level relations among individual events. To overcome the limitations of the existing approaches, in this paper, we propose a method for automatic inference of causal relationships from human written language at conceptual level. To this end, we leverage the characteristics of hierarchy of concepts and linguistic variables created from text, and represent the extracted causal relationships in the form of a Causal Bayesian Network. Our experiments demonstrate superiority of our approach over the existing approaches in inferring complex causal reasoning from the text.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
