用大模型提取文档中所有概念，助力知识库构建
ConExion: Concept Extraction with Large Language Models
- 基于预训练大模型，从文本中挖掘领域内全部概念
- 在两个数据集上F1分数优于现有方法
- 无需标注数据，通过提示词实现无监督抽取
本文提出一种利用预训练大语言模型（LLMs）从文档中提取概念的方法。与传统仅提取关键词以概括内容不同，该方法致力于识别特定领域内所有相关概念，而不仅限于重要信息。在两个广泛使用的基准数据集上的全面评估表明，该方法相比现有最先进技术显著提升了F1分数。此外，研究探索了在模型中使用提示词进行无监督概念提取的潜力。所提取的概念可用于评估本体的领域覆盖度并辅助本体学习，凸显了大语言模型在概念抽取任务中的有效性。代码与数据集已公开，地址为 https://github.com/ISE-FIZKarlsruhe/concept_extraction。
原文摘要 · Abstract (English)
In this paper, an approach for concept extraction from documents using pre-trained large language models (LLMs) is presented. Compared with conventional methods that extract keyphrases summarizing the important information discussed in a document, our approach tackles a more challenging task of extracting all present concepts related to the specific domain, not just the important ones. Through comprehensive evaluations of two widely used benchmark datasets, we demonstrate that our method improves the F1 score compared to state-of-the-art techniques. Additionally, we explore the potential of using prompts within these models for unsupervised concept extraction. The extracted concepts are intended to support domain coverage evaluation of ontologies and facilitate ontology learning, highlighting the effectiveness of LLMs in concept extraction tasks. Our source code and datasets are publicly available at https://github.com/ISE-FIZKarlsruhe/concept_extraction.
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