Maximizing Relation Extraction Potential: A Data-Centric Study to Unveil Challenges and Opportunities
<|reference_start|>Maximizing Relation Extraction Potential: A Data-Centric Study to Unveil Challenges and Opportunities: Relation extraction is a Natural Language Processing task aiming 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 relation 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 https://github.com/anushkasw/MaxRE.<|reference_end|>
@article{swarup2024maximizing,
    title={Maximizing Relation Extraction Potential: A Data-Centric Study to Unveil
  Challenges and Opportunities},
    author={Anushka Swarup, Avanti Bhandarkar, Olivia P. Dizon-Paradis, Ronald
  Wilson, and Damon L. Woodard},
    journal={arXiv preprint arXiv:2409.04934},
    year={2024},
    doi={10.1109/ACCESS.2024.3494737},
    archivePrefix={arXiv},
    eprint={2409.04934},
    primaryClass={cs.CL cs.AI}
}