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

TL;DR

This research suggests that modern relation extractors are not robust to complex data and relation characteristics, and emphasizes pivotal issues, such as contextual ambiguity, correlating relations, long-tail data, and fine-grained relation distributions.

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}.

Figures (17)

• Figure 1: Taxonomy of relation extraction algorithms
• Figure 2: A basic deep learning pipeline for relation extraction
• Figure 3: Different categorizations of the multiple relations and overlapping entity problem
• Figure 4: Average micro-F1 scores for all datasets and relation classifier combinations in the fully supervised setting. The x-axis has been magnified according to the range of scores achieved.
• Figure 5: Average micro-F1 scores for all datasets and joint relation extractor combinations in the fully supervised setting.