Hasso-Plattner-Institut
Prof. Dr. Felix Naumann
  
 

Publications

Comparing Features for Ranking Relationships Between Financial Entities based on Text

Tim Repke, Michael Loster, Ralf Krestel
In Proceedings of the 3rd Data Science for Macro-Modeling (DSMM, workshop at SIGMOD), 5 2017 ACM. accepted

DOI: 10.1145/3077240.3077252

Abstract:

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.

BibTeX file

@inproceedings{Tim2017a,
author = { Tim Repke, Michael Loster, Ralf Krestel },
title = { Comparing Features for Ranking Relationships Between Financial Entities based on Text },
year = { 2017 },
month = { 5 },
abstract = { 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. },
publisher = { ACM },
booktitle = { Proceedings of the 3rd Data Science for Macro-Modeling (DSMM, workshop at SIGMOD) },
isbn = { 978-1-4503-5031-0/17/05 },
priority = { 0 }
}

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last change: Fri, 21 Apr 2017 17:50:57 +0200