Textual Information Extraction Model of Financial Reports

This paper proposes a model to extract textual information from financial reports automatically. It takes event extraction as the core, maps narrative information of financial reports into the concepts of financial accounting field, and forms the integration of heterogeneous data of distributed financial information. This study shows that the model can identify text events in large-scale financial reporting corpus, automatically extract events and theirs attribute information, and convert them into structured data. Moreover, this paper presents and evaluates the effect of the model in extracting information from annual reports of listed companies. The experimental results turn out that the model provides semantic mapping between text events and domain knowledge concepts, which is reasonable and reliable to be applied in the field of financial statement analysis.