Abstract
Nowadays, emerging news on economic events such as acquisitions has a substantial impact on the financial markets. Therefore, it is important to be able to automatically and accurately identify events in news items in a timely manner. For this, one has to be able to process a large amount of heterogeneous sources of unstructured data in order to extract knowledge useful for guiding decision making processes. We propose a Semantics-based Pipeline for Economic Event Detection (SPEED), aiming to extract financial events from emerging news and to annotate these with meta-data, while retaining a speed that is high enough to make real-time use possible. In our implementation of the SPEED pipeline, we reuse some of components of an existing framework and develop new ones, e.g., a high-performance Ontology Gazetteer and a Word Sense Disambiguator. Initial results drive the expectation of a good performance on emerging news.
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Hogenboom, F. et al. (2010). SPEED: A Semantics-Based Pipeline for Economic Event Detection. In: Parsons, J., Saeki, M., Shoval, P., Woo, C., Wand, Y. (eds) Conceptual Modeling – ER 2010. ER 2010. Lecture Notes in Computer Science, vol 6412. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16373-9_34
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DOI: https://doi.org/10.1007/978-3-642-16373-9_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16372-2
Online ISBN: 978-3-642-16373-9
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