Hasso-Plattner-Institut
Prof. Dr. Felix Naumann
  
 

Michael Loster

Hasso-Plattner-Institut
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

Teaching

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)

Publications

Combination of Rule-based and Textual Similarity Approaches to Match Financial Entities

Ahmad Samiei, Ioannis Koumarelas, Michael Loster, Felix Naumann
In of DSMM'16, 2016 ACM.
http://dl.acm.org/citation.cfm?id=2951905

DOI: 10.1145/2951894.2951905

Abstract:

Record linkage is a well studied problem with many years of publication history. Nevertheless, there are many challenges remaining to be addressed, such as the topic addressed by FEIII Challenge 2016. Matching financial entities (FEs) is important for many private and governmental organizations. In this paper we describe the problem of matching such FEs across three datasets: FFIEC, LEI and SEC.

BibTeX file

@inproceedings{Ahmad2016a,
author = { Ahmad Samiei, Ioannis Koumarelas, Michael Loster, Felix Naumann },
title = { Combination of Rule-based and Textual Similarity Approaches to Match Financial Entities },
journal = { Proceedings of the Second International Workshop on Data Science for Macro-Modeling },
year = { 2016 },
month = { 0 },
abstract = { Record linkage is a well studied problem with many years of publication history. Nevertheless, there are many challenges remaining to be addressed, such as the topic addressed by FEIII Challenge 2016. Matching financial entities (FEs) is important for many private and governmental organizations. In this paper we describe the problem of matching such FEs across three datasets: FFIEC, LEI and SEC. },
affiliation = { Hasso Plattner Institute, Potsdam, Germany },
url = { http://dl.acm.org/citation.cfm?id=2951905 },
publisher = { ACM },
series = { DSMM'16 },
isbn = { 978-1-4503-4407-4 },
priority = { 0 }
}

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last change: Thu, 02 Mar 2017 13:52:46 +0100