More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction

TL;DR

This paper reviews the evolution of relation extraction methods, discusses current challenges such as handling more data and complex contexts, and suggests promising future directions for more powerful and open-domain RE systems.

Contribution

It provides a comprehensive review of RE methods, analyzes key challenges, and outlines future research directions for more robust, efficient, and open-domain relation extraction.

Findings

Significant progress from pattern matching to neural networks.

Current challenges include handling large data, complex contexts, and open domains.

Future directions involve developing more powerful, flexible RE systems.

Abstract

Relational facts are an important component of human knowledge, which are hidden in vast amounts of text. In order to extract these facts from text, people have been working on relation extraction (RE) for years. From early pattern matching to current neural networks, existing RE methods have achieved significant progress. Yet with explosion of Web text and emergence of new relations, human knowledge is increasing drastically, and we thus require "more" from RE: a more powerful RE system that can robustly utilize more data, efficiently learn more relations, easily handle more complicated context, and flexibly generalize to more open domains. In this paper, we look back at existing RE methods, analyze key challenges we are facing nowadays, and show promising directions towards more powerful RE. We hope our view can advance this field and inspire more efforts in the community.