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Relation Extraction (RE) is a foundational task of natural languageprocessing. RE seeks to transform raw, unstructured text into structuredknowledge by identifying relational information between entity pairs found intext. RE has numerous uses, such as knowledge graph completion, textsummarization, question-answering, and search querying. The history of REmethods can be roughly organized into four phases: pattern-based RE,statistical-based RE, neural-based RE, and large language model-based RE. Thissurvey begins with an overview of a few exemplary works in the earlier phasesof RE, highlighting limitations and shortcomings to contextualize progress.Next, we review popular benchmarks and critically examine metrics used toassess RE performance. We then discuss distant supervision, a paradigm that hasshaped the development of modern RE methods. Lastly, we review recent RE worksfocusing on denoising and pre-training methods.

An Overview of Distant Supervision for Relation Extraction with a Focus on Denoising and Pre-training Methods