NIED: A Corpus for Numeric Information Extraction from Dataset Descriptions
Abstract: Although a large number of machine learning datasets have been proposed, their descriptions are often insufficiently structured, making it difficult to search datasets using quantitative criteria. To address this issue, we construct NIED, an annotated corpus consisting of 3,926 dataset descriptions collected from academic papers and data repositories, designed for the task of extracting numerical information from dataset descriptions. In addition, we propose a two-stage labeling scheme that distinguishes numerical entities from contextual non-numerical information.
Authors: Moriyuki Kamoto, Akihiro Tamura, Marie Katsurai
Publication venue: JCDL 2025
Reference
M. Kamoto, A. Tamura and M. Katsurai, “NIED: A Corpus for Numeric Information Extraction from Dataset Descriptions,” 2025 ACM/IEEE Joint Conference on Digital Libraries (JCDL), Dekalb, IL, USA, 2025, pp. 241-244, doi: 10.1109/JCDL67857.2025.00036.