A Survey of Deep Learning Methods for Relation Extraction

TL;DR

This survey reviews deep learning approaches for relation extraction, comparing their strengths and weaknesses to guide future research in this important information extraction subfield.

Contribution

It provides a comprehensive comparison of various deep learning models used for relation extraction, highlighting their contributions and limitations.

Findings

Identifies key strengths and weaknesses of DL models for relation extraction

Highlights challenges and potential directions for future research

Summarizes recent advances in deep learning techniques for the task

Abstract

Relation Extraction is an important sub-task of Information Extraction which has the potential of employing deep learning (DL) models with the creation of large datasets using distant supervision. In this review, we compare the contributions and pitfalls of the various DL models that have been used for the task, to help guide the path ahead.