Abstract
This paper introduces a novel method for closed information extraction. The method employs a discriminative approach that incorporates type and entity-specific information to improve relation extraction accuracy, particularly benefiting long-tail relations. Notably, this method demonstrates superior performance compared to state-of-the-art end-to-end generative models. This is especially evident for the problem of large-scale closed information extraction where we are confronted with millions of entities and hundreds of relations. Furthermore, we emphasize the efficiency aspect by leveraging smaller models. In particular, the integration of type-information proves instrumental in achieving performance levels on par with or surpassing those of a larger generative model. This advancement holds promise for more accurate and efficient information extraction techniques.
Abstract (translated)
这篇论文介绍了一种新颖的封闭信息抽取方法。该方法采用判别性方法，结合类型和实体特定的信息来提高关系抽取的准确性，尤其是对于长尾关系有显著改善。值得注意的是，这种方法在性能上优于最先进的端到端生成模型，特别是在大规模封闭信息抽取问题中表现尤为突出，这时我们面临着数百万个实体和数百种关系的挑战。此外，我们还强调了通过使用更小的模型来提高效率的重要性。特别是，类型信息的整合对于实现与更大规模生成模型相媲美甚至超过其性能至关重要。这一进步为更加准确和高效的信提取技术带来了希望。
URL
https://arxiv.org/abs/2506.16348
