Neural relation extraction: a review

Creative Commons License

Aydar M., Bozal O., Ozbay F.

TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, cilt.29, sa.2, ss.1029-1043, 2021 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Derleme
  • Cilt numarası: 29 Sayı: 2
  • Basım Tarihi: 2021
  • Doi Numarası: 10.3906/elk-2005-119
  • Dergi Adı: TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, INSPEC, TR DİZİN (ULAKBİM)
  • Sayfa Sayıları: ss.1029-1043
  • Anahtar Kelimeler: Neural relation extraction, deep learning, pretrained model, distant supervision
  • Yıldız Teknik Üniversitesi Adresli: Hayır

Özet

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.