End-to-end relation extraction (E2ERE) is an important task in information extraction, more so for biomedicine as scientific literature continues to grow exponentially. E2ERE typically involves identifying entities (or named entity recognition (NER)) and associated relations, while most RE tasks simply assume that the entities are provided upfront and end up performing relation classification. E2ERE is inherently more difficult than RE alone given the potential snowball effect of errors from NER leading to more errors in RE. A complex dataset in biomedical E2ERE is the ChemProt dataset (BioCreative VI, 2017) that identifies relations between chemical compounds and genes/proteins in scientific literature. ChemProt is included in all recent biomedical natural language processing benchmarks including BLUE, BLURB, and BigBio. However, its treatment in these benchmarks and in other separate efforts is typically not end-to-end, with few exceptions. In this effort, we employ a span-based pipeline approach to produce a new state-of-the-art E2ERE performance on the ChemProt dataset, resulting in $> 4\%$ improvement in F1-score over the prior best effort. Our results indicate that a straightforward fine-grained tokenization scheme helps span-based approaches excel in E2ERE, especially with regards to handling complex named entities. Our error analysis also identifies a few key failure modes in E2ERE for ChemProt.

翻译：端到端关系抽取（E2ERE）是信息抽取领域的重要任务，在生物医学文献呈指数级增长的背景下尤为关键。E2ERE通常涉及实体识别（即命名实体识别（NER））及其关联关系检测，而大多数关系抽取任务则直接假设实体已预先给定，仅需完成关系分类。由于NER阶段的错误可能产生连锁反应导致关系抽取错误率上升，E2ERE本质上比单纯的关系抽取更具挑战性。生物医学E2ERE领域的典型复杂数据集是ChemProt（BioCreative VI, 2017），该数据集用于识别科学文献中化合物与基因/蛋白质之间的关联关系。该数据集已被纳入BLUE、BLURB和BigBio等最新生物医学自然语言处理基准测试，但除少数例外情况外，这些基准测试及其他独立研究中通常未采用端到端方法处理该数据集。本研究采用基于跨度的流水线方法，在ChemProt数据集上实现了新的最优端到端关系抽取性能，F1分数较先前最佳方法提升超过4%。实验结果表明，简单的细粒度分词方案有助于基于跨度的方法在端到端关系抽取中取得卓越表现，尤其在处理复杂命名实体时效果显著。通过错误分析，我们还识别出ChemProt数据集中端到端关系抽取的若干关键失效模式。