A Data Science Central Community
Detecting Economic Events Using a Semantics-Based Pipeline
http://people.few.eur.nl/fhogenboom/papers/dexa11-speed.pdf
Alexander Hogenboom, Frederik Hogenboom, Flavius Frasincar, Uzay Kaymak, Otto van der Meer, and Kim Schouten
Erasmus University Rotterdam
Abstract.
In today's information-driven global economy, breaking news on economic events such as acquisitions and stock splits has a substantial impact on the financial markets. Therefore, it is important to be able to automatically identify events in news items accurately and in a timely manner. For this purpose, one has to be able to mine a wide variety of heterogeneous sources of unstructured data to extract knowledge that is useful for guiding decision making processes. We propose a Semantics-based Pipeline for Economic Event Detection (SPEED), which aims at extracting nancial events from news articles and annotating these events with meta-data, while retaining a speed that is high enough to make real-time use possible. In our pipeline implementation, we have reused some of the components of an existing framework and developed new ones, such as an Ontology Gazetteer and a Word Sense Disambiguator.
© 2021 TechTarget, Inc.
Powered by
Badges | Report an Issue | Privacy Policy | Terms of Service
Most Popular Content on DSC
To not miss this type of content in the future, subscribe to our newsletter.
Other popular resources
Archives: 2008-2014 | 2015-2016 | 2017-2019 | Book 1 | Book 2 | More
Most popular articles
You need to be a member of AnalyticBridge to add comments!
Join AnalyticBridge