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

• URL: http://arxiv.org/abs/2409.04934v2
• Date: Mon, 25 Nov 2024 20:16:02 GMT
• Title: Maximizing Relation Extraction Potential: A Data-Centric Study to Unveil Challenges and Opportunities
• Authors: Anushka Swarup, Avanti Bhandarkar, Olivia P. Dizon-Paradis, Ronald Wilson, Damon L. Woodard,
• Abstract summary: This paper investigates the possible data-centric characteristics that impede neural relation extraction. It emphasizes pivotal issues, such as contextual ambiguity, correlating relations, long-tail data, and fine-grained relation distributions. It sets a marker for future directions to alleviate these issues, thereby proving to be a critical resource for novice and advanced researchers.
• Score: 3.8087810875611896
• License:
• 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}.

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