Academy & Industry Research Collaboration Center (AIRCC)

Volume 11, Number 20, November 2021

An Evaluation of State-of-the-Art Approaches to Relation Extraction for Usage on Domain-Specific Corpora


Christoph Brandl1, Jens Albrecht1 and Renato Budinich2, 1Nuremberg Institute of Technology Georg Simon Ohm, Germany, 2Fraunhofer Supply Chain Services, Germany


The task of relation extraction aims at classifying the semantic relations between entities in a text. When coupled with named-entity recognition these can be used as the building blocks for an information extraction procedure that results in the construction of a Knowledge Graph. While many NLP libraries support named-entity recognition, there is no off-the-shelf solution for relation extraction.

In this paper, we evaluate and compare several state-of-the-art approaches on a subset of the FewRel data set as well as a manually annotated corpus. The custom corpus contains six relations from the area of market research and is available for public use. Our approach provides guidance for the selection of models and training data for relation extraction in realworld projects.


Relation Extraction, Knowledge Graph, Market Research.