Financial Event Extraction Using Wikipedia-Based Weak Supervision
This addresses the problem of extracting economic events from text for financial analysis, offering a more scalable approach than rule-based or knowledge-base-dependent methods.
The paper tackles financial event extraction by using Wikipedia sections as weak supervision to label sentences describing economic events, eliminating the need for external knowledge bases or financial figures. The method can extract events for companies not in the training data.
Extraction of financial and economic events from text has previously been done mostly using rule-based methods, with more recent works employing machine learning techniques. This work is in line with this latter approach, leveraging relevant Wikipedia sections to extract weak labels for sentences describing economic events. Whereas previous weakly supervised approaches required a knowledge-base of such events, or corresponding financial figures, our approach requires no such additional data, and can be employed to extract economic events related to companies which are not even mentioned in the training data.