Advertisement

Combining Expert Knowledge with NLP for Specialised Applications

Conference paper
  • 473 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12284)

Abstract

Traditionally, there has been a disconnect between custom-built applications used to solve real-world information extraction problems in industry, and automated learning-based approaches developed in academia. Despite approaches such as transfer-based learning, adapting these to more customised solutions where the task and data may be different, and where training data may be largely unavailable, is still hugely problematic, with the result that many systems still need to be custom-built using expert hand-crafted knowledge, and do not scale. In the legal domain, a traditional slow adopter of technology, black box machine learning-based systems are too untrustworthy to be widely used. In industrial settings, the fine-grained highly specialised knowledge of human experts is still critical, and it is not obvious how to integrate this into automated classification systems. In this paper, we examine two case studies from recent work combining this expert human knowledge with automated NLP technologies.

Keywords

Natural language processing Ontologies Information extraction 

References

  1. 1.
    Barré, R.: Sense and nonsense of S&T productivity indicators. Sci. Public Policy 28(4), 259–266 (2001)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Van den Besselaar, P., Heimeriks, G.: Mapping research topics using word-reference co-occurrences: a method and an exploratory case study. Scientometrics 68(3), 377–393 (2006)CrossRefGoogle Scholar
  3. 3.
    Bonnefon, J.F., Shariff, A., Rahwan, I.: The social dilemma of autonomous vehicles. Science 352(6293), 1573–1576 (2016)CrossRefGoogle Scholar
  4. 4.
    Cunningham, H.: GATE, a general architecture for text engineering. Comput. Humanit. 36(2), 223–254 (2002).  https://doi.org/10.1023/A:1014348124664CrossRefGoogle Scholar
  5. 5.
    Daraio, C., et al.: Data integration for research and innovation policy: an ontology-based data management approach. Scientometrics 106(2), 857–871 (2016)CrossRefGoogle Scholar
  6. 6.
    Debackere, K., Luwel, M.: Patent data for monitoring S&T portfolios. In: Moed, H.F., Glänzel, W., Schmoch, U. (eds.) Handbook of Quantitative Science and Technology Research, pp. 569–585. Springer, Dordrecht (2004).  https://doi.org/10.1007/1-4020-2755-9_27CrossRefGoogle Scholar
  7. 7.
    Rafols, I., Porter, A.L., Leydesdorff, L.: Science overlay maps: a new tool for research policy and library management. J. Am. Soc. Inform. Sci. Technol. 61(9), 1871–1887 (2010)CrossRefGoogle Scholar
  8. 8.
    Shiffrin, R.M., Börner, K.: Mapping knowledge domains. PNAS 101, 5183–5185 (2004) CrossRefGoogle Scholar
  9. 9.
    Šubelj, L., van Eck, N.J., Waltman, L.: Clustering scientific publications based on citation relations: a systematic comparison of different methods. PloS ONE 11(4), e0154404 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of SheffieldSheffieldUK

Personalised recommendations