Neural relation extraction: a review

Authors

MEHMET AYDAR
ÖZGE BOZAL
FURKAN ÖZBAY

DOI

10.3906/elk-2005-119

Abstract

Neural relation extraction discovers semantic relations between entities from unstructured text using deeplearning methods. In this study, we make a clear categorization of the existing relation extraction methods in termsof data expressiveness and data supervision, and present a comprehensive and comparative review. We describe theevaluation methodologies and the datasets used for model assessment. We explicitly state the common challenges inrelation extraction task and point out the potential of the pretrained models to solve them. Accordingly, we investigateadditional research directions and improvement ideas in this field.

Keywords

Neural relation extraction, deep learning, pretrained model, distant supervision

First Page

1029

Last Page

1043

Recommended Citation

AYDAR, MEHMET; BOZAL, ÖZGE; and ÖZBAY, FURKAN (2021) "Neural relation extraction: a review," Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 29: No. 2, Article 35. https://doi.org/10.3906/elk-2005-119
Available at: https://journals.tubitak.gov.tr/elektrik/vol29/iss2/35