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Proceedings of the 20th ACM international conference on Information and knowledge management - CIKM '11 - Accurate information extraction for quantitative financial events by Malik, Hassan H.; Bhardwaj, Vikas S.; Fiorletta, Huascar is a scholarly article available to read on EtoBox.

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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%.

Author

Malik, Hassan H.; Bhardwaj, Vikas S.; Fiorletta, Huascar

Publisher

ACM Press

Published

2011

Language

EN