Transforming classifier scores into accurate multiclass probability estimates
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Integrating top-down and bottom-up strategies in a text processing system
ANLC '88 Proceedings of the second conference on Applied natural language processing
Automatic extraction of facts from press releases to generate news stories
ANLC '92 Proceedings of the third conference on Applied natural language processing
Foundations and Trends in Databases
Single pass text classification by direct feature weighting
Knowledge and Information Systems
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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%.