从财务报表摘要中发现稀有因果知识
Discovery of Rare Causal Knowledge from Financial Statement Summaries
AI总结：
本文面向企业日文财务报表摘要，提出结合扩展语言本体、机器学习与句法模式的三步流程，以识别并提取最稀有的因果知识。
摘要：
如果气温受到抑制并导致一个凉爽的夏天，会发生什么？人们很容易想象，空调、冰淇淋或啤酒的销售会因此受到抑制。较不明显的是，农产品运输可能会延迟，或者隔音材料的销售可能会下降。提取此类因果知识的能力很重要，但同样重要的是，要区分已知的因果对与那些可能未知或稀有的因果对。因此，本文提出一种从企业编制的日文财务报表摘要中提取稀有因果知识的方法。该方法包括三个步骤：首先，使用基于扩展语言本体的机器学习方法，从摘要中提取包含因果知识的句子；其次，利用句法模式从提取出的句子中获取因果知识；最后，从已获得的知识中提取最稀有的因果知识。
英文摘要：
What would happen if temperatures were subdued and result in a cool summer? One can easily imagine that air conditioner, ice cream or beer sales would be suppressed as a result of this. Less obvious is that agricultural shipments might be delayed, or that sound proofing material sales might decrease. The ability to extract such causal knowledge is important, but it is also important to distinguish between cause-effect pairs that are known and those that are likely to be unknown, or rare. Therefore, in this paper, we propose a method for extracting rare causal knowledge from Japanese financial statement summaries produced by companies. Our method consists of three steps. First, it extracts sentences that include causal knowledge from the summaries using a machine learning method based on an extended language ontology. Second, it obtains causal knowledge from the extracted sentences using syntactic patterns. Finally, it extracts the rarest causal knowledge from the knowledge it has obtained.
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。
