用大模型代理解决复杂表格语义标注难题，效率提升显著。
An LLM Agent-Based Complex Semantic Table Annotation Approach
- 设计五种外部工具，动态选择标注策略应对表格复杂性。
- 在两个挑战数据集上表现优于现有方法，减少70%耗时与60% token使用。
- 适合需要高效精准表格标注的科研与工业场景。
语义表格标注（STA）任务包含列类型标注（CTA）和单元格实体标注（CEA），将表格内容映射到本体实体，在多种语义应用中至关重要。然而，复杂表格常面临列名或单元格值语义丢失、本体层级严格、同义词、拼写错误及缩写等问题，影响标注准确率。本文提出基于大语言模型（LLM）代理的CTA与CEA方法。设计并实现五个基于ReAct框架的外部工具，结合定制提示，使标注代理可根据表格特征动态选择策略。在SemTab挑战赛的Tough Tables与BiodivTab数据集上进行实验，这些数据集包含前述挑战。所提方法在多个指标上超越现有技术。此外，通过引入莱文斯坦距离减少冗余标注，实现时间成本降低70%、LLM token消耗减少60%，提供高效低成本的解决方案。
原文摘要 · Abstract (English)
The Semantic Table Annotation (STA) task, which includes Column Type Annotation (CTA) and Cell Entity Annotation (CEA), maps table contents to ontology entities and plays important roles in various semantic applications. However, complex tables often pose challenges such as semantic loss of column names or cell values, strict ontological hierarchy requirements, homonyms, spelling errors, and abbreviations, which hinder annotation accuracy. To address these issues, this paper proposes an LLM-based agent approach for CTA and CEA. We design and implement five external tools with tailored prompts based on the ReAct framework, enabling the STA agent to dynamically select suitable annotation strategies depending on table characteristics. Experiments are conducted on the Tough Tables and BiodivTab datasets from the SemTab challenge, which contain the aforementioned challenges. Our method outperforms existing approaches across various metrics. Furthermore, by leveraging Levenshtein distance to reduce redundant annotations, we achieve a 70% reduction in time costs and a 60% reduction in LLM token usage, providing an efficient and cost-effective solution for STA.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。