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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Hampton, P., Wang, H., Blackburn, W., Lin, Z. (2016). Automated Sequence Tagging: Applications in Financial Hybrid Systems. In: Bramer, M., Petridis, M. (eds) Research and Development in Intelligent Systems XXXIII. SGAI 2016. Springer, Cham. https://doi.org/10.1007/978-3-319-47175-4_22
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DOI: https://doi.org/10.1007/978-3-319-47175-4_22
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