Maximizing Relation Extraction Potential: A Data-Centric Study to Unveil Challenges and Opportunities
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arXiv
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| Format: | Preprint |
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2024
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| author | Swarup, Anushka Bhandarkar, Avanti Dizon-Paradis, Olivia P. Wilson, Ronald Woodard, Damon L. |
| author_facet | Swarup, Anushka Bhandarkar, Avanti Dizon-Paradis, Olivia P. Wilson, Ronald Woodard, Damon L. |
| contents | 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}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_04934 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Maximizing Relation Extraction Potential: A Data-Centric Study to Unveil Challenges and Opportunities Swarup, Anushka Bhandarkar, Avanti Dizon-Paradis, Olivia P. Wilson, Ronald Woodard, Damon L. Computation and Language Artificial Intelligence 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}. |
| title | Maximizing Relation Extraction Potential: A Data-Centric Study to Unveil Challenges and Opportunities |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2409.04934 |