Wiki-TabNER:Advancing Table Interpretation Through Named Entity Recognition

arXiv:2403.04577v1 Announce Type: cross
Abstract: Web tables contain a large amount of valuable knowledge and have inspired tabular language models aimed at tackling table interpretation (TI) tasks. In this paper, we analyse a widely used benchmark dataset for evaluation of TI tasks, particularly focusing on the entity linking task. Our analysis reveals that this dataset is overly simplified, potentially reducing its effectiveness for thorough evaluation and failing to accurately represent tables as they appear in the real-world. To overcome this …

abstract analysis arxiv benchmark cs.ai cs.cl dataset evaluation interpretation knowledge language language models paper recognition table tables tabular tasks through type web