Authors: Alexander Hogenboom, Flavius Frasincar, Frederik Hogenboom, Kim Schouten et al., Otto van der Meer, Uzay Kaymak
Tags: 2010, conceptual modeling
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.Read the full paper here: https://link.springer.com/chapter/10.1007/978-3-642-16373-9_34
