Cogprints

Automated Understanding of Financial Statements Using Neural Networks and Semantic Grammars

Markovitch, J. S. (1995) Automated Understanding of Financial Statements Using Neural Networks and Semantic Grammars. [Conference Paper]

Full text available as:

[img]
Preview
PDF
116Kb

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.

Item Type:Conference Paper
Keywords:neural networks semantic grammars
Subjects:Computer Science > Language
Computer Science > Neural Nets
Computer Science > Artificial Intelligence
ID Code:2905
Deposited By:Markovitch, J. S.
Deposited On:26 Apr 2003
Last Modified:11 Mar 2011 08:55

Metadata

Repository Staff Only: item control page