Abstract
Joint entity-relation extraction (JERE) identifies both entities and their relationships simultaneously. Traditional machine-learning based approaches to performing this task require a large corpus of annotated data and lack the ability to easily incorporate domain specific information in the construction of the model. Therefore, creating a model for JERE is often labor intensive, time consuming, and elaboration intolerant. In this paper, we propose harnessing the capabilities of generative pretrained large language models (LLMs) and the knowledge representation and reasoning capabilities of Answer Set Programming (ASP) to perform JERE. We present a generic workflow for JERE using LLMs and ASP. The workflow is generic in the sense that it can be applied for JERE in any domain. It takes advantage of LLM's capability in natural language understanding in that it works directly with unannotated text. It exploits the elaboration tolerant feature of ASP in that no modification of its core program is required when additional domain specific knowledge, in the form of type specifications, is found and needs to be used. We demonstrate the usefulness of the proposed workflow through experiments with limited training data on three well-known benchmarks for JERE. The results of our experiments show that the LLM + ASP workflow is better than state-of-the-art JERE systems in several categories with only 10\% of training data. It is able to achieve a 2.5 times (35\% over 15\%) improvement in the Relation Extraction task for the SciERC corpus, one of the most difficult benchmarks.
Abstract (translated)
联合实体关系抽取（Joint Entity-Relation Extraction，JERE）同时识别实体及其之间的关系。传统基于机器学习的方法执行此任务时需要大量标注数据，并且缺乏轻松整合特定领域信息的能力。因此，创建一个用于JERE的模型通常是劳动密集型、耗时长并且对详细说明不耐受的。在本文中，我们提出利用生成式预训练大型语言模型（LLMs）和答案集编程（ASP）的知识表示与推理能力来执行JERE任务。我们提出了使用LLM和ASP进行JERE的一个通用工作流程。该工作流程是通用性的，在任何领域都可以应用。它利用了LLM在自然语言理解中的优势，可以直接处理未标注的文本；同时，通过其答案集编程（ASP）中的详尽性容忍特性，无需修改核心程序即可轻松添加特定领域的类型规范等知识。 我们通过在三个著名的JERE基准测试中使用有限训练数据进行实验来展示该工作流程的有效性。我们的实验证明，在只有10%的训练数据下，LLM + ASP的工作流优于当前最先进的JERE系统。对于最困难的基准之一——SciERC语料库中的关系抽取任务，它可以实现2.5倍（从15%到35%）的性能提升。
URL
https://arxiv.org/abs/2508.12611
