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Automated Sequence Tagging: Applications in Financial Hybrid Systems

  • Peter HamptonEmail author
  • Hui Wang
  • William Blackburn
  • Zhiwei Lin
Conference paper

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.

Keywords

Finance XBRL Information Extraction Representation 

References

  1. 1.
    Li, N., Desheng Dash, W.: Using text mining and sentiment analysis for online forums hotspot detection and forecast. Decis. Support Syst. 48(2), 354–368 (2010)CrossRefGoogle Scholar
  2. 2.
    Loughran, T., McDonald, B.: When is a liability not a liability? Textual analysis, dictionaries, and 10Ks. J. Financ. 66(1), 35–65 (2011)CrossRefGoogle Scholar
  3. 3.
    Bodnaruk, A., Loughran, T., McDonald, B.: Using 10-K text to gauge financial constraints. J. Financ. Quant. Anal. 50, 4 (2015)CrossRefGoogle Scholar
  4. 4.
    Fisher, I.E., Garnsey, M.R., Hughes, M.E.: Natural language processing in accounting, auditing and finance: a synthesis of the literature with a roadmap for future research. Intell. Syst. Account. Financ. Manag. (2016)Google Scholar
  5. 5.
    Hirschberg, J., Manning, C.D.: Advances in natural language processing. Science 349(6245), 261–266 (2015)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Saggion, H., et al.: Ontology-based information extraction for business intelligence. Springer, Berlin (2007)Google Scholar
  7. 7.
    Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retr. 2(1–2), 1–135 (2008)CrossRefGoogle Scholar
  8. 8.
    Kearney, C., Liu, S.: Textual sentiment in finance: a survey of methods and models. Int. Rev. Financ. Anal. 33, 171–185 (2014)CrossRefGoogle Scholar
  9. 9.
    Hampton, P.J., Wang, H., Blackburn, W.: A hybrid ensemble for classifying and repurposing financial entities. In: Research and Development in Intelligent Systems XXXII, pp. 197–202. Springer (2015)Google Scholar
  10. 10.
    Loughran, T., McDonald, B.: Textual analysis in accounting and finance: a survey. J. Account. Res. (2016)Google Scholar
  11. 11.
    Burr, G.W., et al.: Experimental demonstration and tolerancing of a large-scale neural network (165 000 Synapses) using phase-change memory as the synaptic weight element. IEEE Trans. Electron Dev. 62(11), 3498–3507 (2015)Google Scholar
  12. 12.
    Shaalan, K.: A survey of arabic named entity recognition and classification. Comput. Linguist. 40(2), 469–510 (2014)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Marrero, M., et al.: Named entity recognition: fallacies, challenges and opportunities. Comput. Stand. Interfaces 35(5), 482–489 (2013)Google Scholar
  14. 14.
    Nadeau, D., Sekine, S.: A survey of named entity recognition and classification. Lingvist. Investig. 30(1), 3–26 (2007)CrossRefGoogle Scholar
  15. 15.
    Reeve, L., Han, H.: Survey of semantic annotation platforms. In: Proceedings of the 2005 ACM Symposium on Applied computing. ACM (2005)Google Scholar
  16. 16.
    Loughran, T., McDonald, B.: When is a liability not a liability? Textual analysis, dictionaries, and 10Ks. The Journal of Finance 66(1), 35–65 (2011)CrossRefGoogle Scholar
  17. 17.
    Wisniewski, T.P., Yekini, L.S.: Stock market returns and the content of annual report narratives. In: Accounting Forum, vol. 39, no. 4. Elsevier (2015)Google Scholar
  18. 18.
    Troshani, I., Parker, L.D., Lymer, A.: Institutionalising XBRL for financial reporting: resorting to regulation. Account. Bus. Res. 45(2), 196–228 (2015)CrossRefGoogle Scholar
  19. 19.
    Burdick, D., et al.: Extracting, linking and integrating data from public sources: a financial case study. SSRN (2015)Google Scholar
  20. 20.
    Chiticariu, L., Li, Y., Reiss, F.R.: Rule-based information extraction is dead! Long live rule-based information extraction systems!. In: EMNLP, no. 10 (2013)Google Scholar
  21. 21.
    Rao, D., McNamee, P., Dredze, M.: Entity linking: finding extracted entities in a knowledge base. In: Multi-source, Multilingual Information Extraction and Summarization, pp. 93–115. Springer, Berlin (2013)Google Scholar
  22. 22.
    Russell, S., Norvig, P.: Artificial Intelligence—A Modern Approach. Prentice-Hall, Egnlewood Cliffs, vol. 25, p. 27 (2013)Google Scholar
  23. 23.
    Rocktschel, T., Weidlich, M., Leser, U.: ChemSpot: a hybrid system for chemical named entity recognition. Bioinformatics 28(12), 1633–1640 (2012)CrossRefGoogle Scholar
  24. 24.
    Califf, M.E., Mooney, R.J.: Bottom-up relational learning of pattern matching rules for information extraction. J. Mach. Learn. Res. 4, 177–210 (2003)Google Scholar
  25. 25.
    Morwal, S., Jahan, N., Chopra, D.: Named entity recognition using hidden Markov model (HMM). Int. J. Nat. Lang. Comput. (IJNLC) 1(4), 15–23 (2012)CrossRefGoogle Scholar
  26. 26.
    Lafferty, J., McCallum, A., Pereira, F.C.N.: Conditional random fields: probabilistic models for segmenting and labeling sequence data (2001)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Peter Hampton
    • 1
    Email author
  • Hui Wang
    • 1
  • William Blackburn
    • 1
  • Zhiwei Lin
    • 1
  1. 1.Artificial Intelligence and Applications Research GroupUlster UniversityNewtownabbeyUK

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