Comparing Features for Ranking Relationships Between Financial Entities based on Text
Tim Repke, Michael Loster, Ralf Krestel
Our paper "Comparing Features for Ranking Relationships Between Financial Entities based on Text" has been accepted at the Data Science for Macro-Modeling with Financial and Economic Datasets (DSMM 2017) workshop. The workshop is co-located with SIGMOD/PODS 2017 in Chicago, IL, USA.
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.