Automated Understanding of Financial Statements Using Neural Networks and Semantic Grammars

Abstract

This article discusses how neural networks and semantic grammars may be used to locate and understand financial statements embedded in news stories received from on-line news wires. A neural net is used to identify where in the news story a financial statement appears to begin. A grammar then is applied to this text in an effort to extract specific facts from the financial statement. Applying grammars to financial statements presents unique parsing problems since the dollar amounts of financial statements are typically arranged in multiple columns, with small paragraphs of text above each column. Text therefore is meant to be read both vertically and horizontally, in contrast to ordinary news text, which is read only horizontally.