PlanetOnto: From News Publishing to Integrated Knowledge Management Support
IEEE Intelligent Systems
Architectural elements of language engineering robustness
Natural Language Engineering
KIM – a semantic platform for information extraction and retrieval
Natural Language Engineering
Structural Semantic Interconnections: A Knowledge-Based Approach to Word Sense Disambiguation
IEEE Transactions on Pattern Analysis and Machine Intelligence
GATE: an architecture for development of robust HLT applications
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Feature-rich part-of-speech tagging with a cyclic dependency network
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Text Understanding Agents and the Semantic Web
HICSS '06 Proceedings of the 39th Annual Hawaii International Conference on System Sciences - Volume 03
The Penn-Lehman Automated Trading Project
IEEE Intelligent Systems
Natural language technology for information integration in business intelligence
BIS'07 Proceedings of the 10th international conference on Business information systems
Semi-automatic financial events discovery based on lexico-semantic patterns
International Journal of Web Engineering and Technology
Identification of fine grained feature based event and sentiment phrases from business news stories
Proceedings of the International Conference on Web Intelligence, Mining and Semantics
A lexico-semantic pattern language for learning ontology instances from text
Web Semantics: Science, Services and Agents on the World Wide Web
Semantics-based information extraction for detecting economic events
Multimedia Tools and Applications
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Due to its high productivity at relatively low costs, algorithmic trading has become increasingly popular over the last few years. As news can improve the returns generated by algorithmic trading, there is a growing need to use online news information in algorithmic trading in order to react real-time to market events. The biggest challenge is to automate the recognition of financial events from Web news items as an important input next to stock prices for algorithmic trading. In this position paper, we propose a multi-disciplinary approach to financial events recognition in news for algorithmic trading called FERNAT, using techniques from finance, text mining, artificial intelligence, and the Semantic Web.