Supervised NRE models suffer from the lack of large-scale high-quality training data, since manually labeling data is time-consuming and human-intensive. To alleviate this issue, distant supervision (DS) assumption has been used to automatically label data by aligning existing KGs with plain text.

Utilizing More Data: Methods

Supervised RE methods heavily rely on expensive human
annotations, while distant supervision introduces more auto-labeled data to alleviate this issue. Designing schemas to obtain high-quality and high-coverage data to train robust RE models still remains a problem to be explored.

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Feasible Approaches Leading to Better RE
Abilities

Han, X. (n.d.). More Data, More Relations, More Context and More Openness:
A Review and Outlook for Relation Extraction *, Retrieved June 4, 2022, from https://arxiv.org/pdf/2004.03186.pdf

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

Utilizing More Data

Lots of long-tail relations only contain a handful of training examples. However, it is hard for conventional RE methods to well generalize relation patterns from limited examples like humans. Therefore, developing efficient learning schemas
to make better use of limited or few-shot examples is a potential research direction.

Performing More Efficient Learning

Many relational facts are expressed in complicated context (e.g. multiple sentences or even documents), while most existing RE models focus on extracting intra-sentence relations. To cover those complex facts, it is valuable to investigate RE in more complicated context.

Handling More Complicated Context

Conventional RE frameworks are generally designed for pre-defined relations. Therefore, how to automatically detect undefined relations in open domains remains an open problem.

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