Prof. Dr. Felix Naumann

Michael Loster

für Softwaresystemtechnik
Prof.-Dr.-Helmert-Straße 2-3
D-14482 Potsdam

Phone: +49 331 5509 286
Fax: +49 331 5509 287
Room: G-3.2.10
Email: Michael Loster


Research Interests

  • Text Mining
  • Opinion Mining
  • Natural Language Processing
  • Machine Learning
  • Data Mining


Bachelor Projects:

  • Analytical Tools for Semantic Company Networks (2015/2016)

Master Thesis:

  • From Text to Facts: Relation Extraction on German Company Websites (Tanja Bergmann, 2016)
  • German Organization Name Part Classification (Manuel Hegner, 2016)


Comparing Features for Ranking Relationships Between Financial Entities Based on Text

Repke, Tim; Loster, Michael; Krestel, Ralf in Proceedings of the 3rd International Workshop on Data Science for Macro--Modeling with Financial and Economic Datasets page 12:1--12:2 . New York, NY, USA , ACM , 2017 .

Evaluating the credibility of a company is an important and complex task for financial experts. When estimating the risk associated with a potential asset, analysts rely on large amounts of data from a variety of different sources, such as newspapers, stock market trends, and bank statements. Finding relevant information, such as relationships between financial entities, in mostly unstructured data is a tedious task and examining all sources by hand quickly becomes infeasible. In this paper, we propose an approach to rank extracted relationships based on text snippets, such that important information can be displayed more prominently. Our experiments with different numerical representations of text have shown, that ensemble of methods performs best on labelled data provided for the FEIII Challenge 2017.
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Further Information
Tags business_communication isg web_science