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

Autor:	Tobias Deußer, Syed Musharraf Ali, Lars Hillebrand, Desiana Nurchalifah, Basil Jacob, Christian Bauckhage, Rafet Sifa
Jazyk:	angličtina
Rok vydání:	2022
Předmět:	FOS: Computer and information sciences machine learning Computer Science - Computation and Language relation extraction named entity recognition text mining natural language processing Computation and Language (cs.CL)
Popis:	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 F1 score to better model the inherently fuzzy borders of the entity pairs of a relation in this domain. Accepted at ICMLA 2022, 6 pages, 5 tables
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f57fe4e10c91967d8c0551737bae50f5 http://arxiv.org/abs/2210.09163 Zobrazit plný text záznamu