Maximizing Relation Extraction Potential: A Data-Centric Study to Unveil Challenges and Opportunities
This study identifies key bottlenecks for researchers and practitioners in information extraction, though it is incremental as it compiles existing challenges without proposing new methods.
The paper investigates data-centric challenges that hinder neural relation extraction, finding that state-of-the-art models are not robust to complex data characteristics like contextual ambiguity and long-tail distributions, based on experiments with 15 algorithms and seven datasets.
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}.