Under CopyrightDeußer, TobiasTobiasDeußerAli, Syed MusharrafSyed MusharrafAliHillebrand, Lars PatrickLars PatrickHillebrandNurchalifah, Desiana DienDesiana DienNurchalifahJacob, BasilBasilJacobBauckhage, ChristianChristianBauckhageSifa, RafetRafetSifa2023-05-082023-05-082022-12https://publica.fraunhofer.de/handle/publica/441403https://doi.org/10.24406/publica-131910.1109/ICMLA55696.2022.0025410.24406/publica-1319We 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.entext miningnatural language processingrelation extractionnamed entity recognitionmachine learningconference paper