Neural relation extraction: a review

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Aydar M., Bozal O., Ozbay F.

TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, vol.29, no.2, pp.1029-1043, 2021 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Review
  • Volume: 29 Issue: 2
  • Publication Date: 2021
  • Doi Number: 10.3906/elk-2005-119
  • Journal Name: TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, INSPEC, TR DİZİN (ULAKBİM)
  • Page Numbers: pp.1029-1043
  • Keywords: Neural relation extraction, deep learning, pretrained model, distant supervision
  • Yıldız Technical University Affiliated: No

Abstract

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