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Automatic Detection of Uncertain Statements in the Financial Domain

  • Christoph Kilian TheilEmail author
  • Sanja Štajner
  • Heiner Stuckenschmidt
  • Simone Paolo Ponzetto
Conference paper
  • 629 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10762)

Abstract

The automatic detection of uncertain statements can benefit NLP tasks such as deception detection and information extraction. Furthermore, it can enable new analyses in social sciences such as business where the quantification of uncertainty or risk plays a significant role. Thus, for the first time, we approached the automatic detection of uncertain statements as a binary sentence classification task on the transcripts of spoken language in the financial domain. We created a new dataset and – besides using bag-of-words, part-of-speech tags, and dictionaries – developed rule-based features tailored to our task. Finally, we analyzed systematically, which features perform best in the financial domain as opposed to the previously researched encyclopedic domain.

Keywords

Automatic uncertainty detection Binary sentence classification Financial domain 

Notes

Acknowledgments

We thank Alexander Diete for his help with the data acquisition and technical advice as well as Clemens Müller for his help with the annotation. This work was supported by the SFB 884 on the Political Economy of Reforms at the University of Mannheim (project C4), funded by the German Research Foundation (DFG).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Christoph Kilian Theil
    • 1
    Email author
  • Sanja Štajner
    • 1
  • Heiner Stuckenschmidt
    • 1
  • Simone Paolo Ponzetto
    • 1
  1. 1.Data and Web Science GroupUniversity of MannheimMannheimGermany

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