Maximizing Relation Extraction Potential: A Data-Centric Study to Unveil Challenges and Opportunities

TL;DR

This study analyzes the performance of 15 state-of-the-art relation extraction models across seven datasets, revealing their vulnerabilities to complex data characteristics like ambiguity and long-tail distributions, and suggests future research directions.

Contribution

It provides a comprehensive data-centric performance analysis of modern relation extractors, highlighting key challenges and proposing future research directions.

Findings

Modern relation extractors struggle with complex data characteristics.

Contextual ambiguity and long-tail data significantly impact extraction performance.

The study offers insights for improving robustness in relation extraction models.

Abstract

Relation extraction is a Natural Language Processing task that aims to extract relationships from textual data. It is a critical step for information extraction. Due to its wide-scale applicability, research in relation extraction has rapidly scaled to using highly advanced neural networks. Despite their computational superiority, modern relation extractors fail to handle complicated extraction scenarios. However, a comprehensive performance analysis of the state-of-the-art extractors that compile these challenges has been missing from the literature, and this paper aims to bridge this gap. The goal has been to investigate the possible data-centric characteristics that impede neural relation extraction. Based on extensive experiments conducted using 15 state-of-the-art relation extraction algorithms ranging from recurrent architectures to large language models and seven large-scale datasets, this research suggests that modern relation extractors are not robust to complex data and relation characteristics. It emphasizes pivotal issues, such as contextual ambiguity, correlating relations, long-tail data, and fine-grained relation distributions. In addition, it sets a marker for future directions to alleviate these issues, thereby proving to be a critical resource for novice and advanced researchers. Efficient handling of the challenges described can have significant implications for the field of information extraction, which is a critical part of popular systems such as search engines and chatbots. Data and relevant code can be found at \url{https://aaig.ece.ufl.edu/projects/relation-extraction}.