Accurate Information Extraction for Quantitative Financial Events

摘要

In this paper, we present: a novel financial event extraction system that achieves very high extraction quality by combining the outcome of statistical classifiers with a set of rules. Using expert-annotated press releases as training data, and novel feature generation schemes, our system learns multiple binary classifiers for each "slot" in a financial event. At runtime, common parsing and search indexing methods are used to normalize incoming press releases and to identify candidate event "slots". Rules are applied on candidates that satisfy a combination of classifiers, and the system confidence on extracted events is estimated using a unique confidence model learned from training data. We present results of experiments performed on European corporate press releases for extracting dividend events, and show that our system achieves a precision of 96% and a recall of 79%.