KPI-EDGAR: A Novel Dataset and Accompanying Metric for Relation Extraction from Financial Documents

We introduce KPI-EDGAR, a novel dataset for Joint Named Entity Recognition and Relation Extraction building on financial reports uploaded to the Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system, where the main objective is to extract Key Performance Indicators (KPIs) from financial documents and link them to their numerical values and other attributes. We further provide four accompanying baselines for benchmarking potential future research. Additionally, we propose a new way of measuring the success of said extraction process by incorporating a word-level weighting scheme into the conventional F$_1$ score to better model the inherently fuzzy borders of the entity pairs of a relation in this domain.

  • Published in:
    International Conference on Machine Learning and Applications
  • Type:
    Inproceedings
  • Authors:
    Deußer, Tobias; Ali, Syed Musharraf; Hillebrand, Lars; Nurchalifah, Desiana; Jacob, Basil; Bauckhage, Christian; Sifa, Rafet
  • Year:
    2022

Citation information

Deußer, Tobias; Ali, Syed Musharraf; Hillebrand, Lars; Nurchalifah, Desiana; Jacob, Basil; Bauckhage, Christian; Sifa, Rafet: KPI-EDGAR: A Novel Dataset and Accompanying Metric for Relation Extraction from Financial Documents, International Conference on Machine Learning and Applications, 2022, https://ieeexplore.ieee.org/document/10069806, Deusser.etal.2022a,

Associated Lamarr Researchers

lamarr institute person Bauckhage Christian - Lamarr Institute for Machine Learning (ML) and Artificial Intelligence (AI)

Prof. Dr. Christian Bauckhage

Director to the profile
Prof. Dr. Rafet Sifa

Prof. Dr. Rafet Sifa

Principal Investigator Hybrid ML to the profile