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Erschienen in:
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2016 | OriginalPaper | Buchkapitel

Automated Sequence Tagging: Applications in Financial Hybrid Systems

verfasst von : Peter Hampton, Hui Wang, William Blackburn, Zhiwei Lin

Erschienen in: Research and Development in Intelligent Systems XXXIII

Verlag: Springer International Publishing

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Abstract

Internal data published by a firm regarding their financial position, governance, people and reaction to market conditions are all believed to impact the underlying company’s valuation. An abundance of heterogeneous information coupled with the ever increasing processing power of machines, narrow AI applications are now managing investment positions and making decisions on behalf of humans. As unstructured data becomes more common, disambiguating structure from text-based documents remains an attractive research goal in the Finance and Investment industry. It has been found that statistical approaches are considered high risk in industrial applications and deterministic methods are typically preferred. In this paper we experiment with hybrid (ensemble) approaches for Named Entity Recognition to reduce implementation and run time risk involved with modern stochastic methods.

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Vorheriges Kapitel SELFBACK—Activity Recognition for Self-management of Low Back Pain
Nächstes Kapitel A Method of Rule Induction for Predicting and Describing Future Alarms in a Telecommunication Network
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Metadaten
Titel
Automated Sequence Tagging: Applications in Financial Hybrid Systems
verfasst von
Peter Hampton
Hui Wang
William Blackburn
Zhiwei Lin
Copyright-Jahr
2016
Buch
Research and Development in Intelligent Systems XXXIII

Print ISBN: 978-3-319-47174-7

Electronic ISBN: 978-3-319-47175-4

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-47175-4_22