Wiki-TabNER：将命名实体识别集成到维基百科表格中
Wiki-TabNER: Integrating Named Entity Recognition into Wikipedia Tables
AI总结：
针对现有表格解释数据集过于简化的问题，本文构建了更具挑战性的 Wiki-TabNER 数据集，并提出提示框架评估大型语言模型在表格内命名实体识别任务上的表现。
摘要：
多年来，解决表格解释任务的兴趣不断增长，但仍然依赖于可能过于简化的现有数据集。这可能会降低数据集在全面评估中的有效性，并且无法准确表示现实世界中出现的表格。为了丰富现有的基准数据集，我们提取并标注了一个新的、更具挑战性的数据集。提出的 Wiki-TabNER 数据集具有复杂的表格特征，每个单元格包含多个实体，并使用 DBpedia 类对命名实体进行标注。该数据集专为处理表格内的命名实体识别（NER）任务而设计，但也可用作评估实体链接任务的更具挑战性的数据集。在本文中，我们描述了 Wiki-TabNER 数据集的显著特征和标注过程。此外，我们提出了一个提示框架，用于在表格内 NER 任务上评估新的大型语言模型。最后，我们进行了定性分析，以深入了解模型遇到的挑战并理解所提出数据集的局限性。
英文摘要：
Interest in solving table interpretation tasks has grown over the years, yet it still relies on existing datasets that may be overly simplified. This is potentially reducing the effectiveness of the dataset for thorough evaluation and failing to accurately represent tables as they appear in the real-world. To enrich the existing benchmark datasets, we extract and annotate a new, more challenging dataset. The proposed Wiki-TabNER dataset features complex tables containing several entities per cell, with named entities labeled using DBpedia classes. This dataset is specifically designed to address named entity recognition (NER) task within tables, but it can also be used as a more challenging dataset for evaluating the entity linking task. In this paper we describe the distinguishing features of the Wiki-TabNER dataset and the labeling process. In addition, we propose a prompting framework for evaluating the new large language models on the within tables NER task. Finally, we perform qualitative analysis to gain insights into the challenges encountered by the models and to understand the limitations of the proposed~dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
