Domain-Driven News Representation Using Conditional Attribute-Value Pairs

  • Mihail Minev
  • Christoph Schommer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8173)


Financial news carry information about economical figures and indicators. However, these texts are mostly unstructured and consequently hard to be processed in an automatic way. In this paper, we present a representation formalism that supports a linguistic composition for machine learning tasks. We show an innovative approach to structuring financial texts by extracting principal indicators. Considering announcements in the monetary policy domain, we distinguish between attributes and their values and argue that attributes are to be represented as an aggregated set of economic terms, keeping their values as corresponding conditional expressions. We close with a critical discussion and future perspectives.


Feature Extraction Text Representation Financial News 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Mihail Minev
    • 1
  • Christoph Schommer
    • 1
  1. 1.Interdisciplinary Lab for Intelligent and Adaptive Systems, Computer Science and Communications Research UnitUniversity of LuxembourgLuxembourg

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