MN-DS: A Multilabeled News Dataset for News Articles Hierarchical Classification

December 22, 2022 ยท Declared Dead ยท ๐Ÿ› International Conference on Data Technologies and Applications

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Authors Alina Petukhova, Nuno Fachada arXiv ID 2212.12061 Category cs.CL: Computation & Language Cross-listed cs.AI, cs.LG Citations 16 Venue International Conference on Data Technologies and Applications Last Checked 4 months ago
Abstract
This article presents a dataset of 10,917 news articles with hierarchical news categories collected between 1 January 2019 and 31 December 2019. We manually labeled the articles based on a hierarchical taxonomy with 17 first-level and 109 second-level categories. This dataset can be used to train machine learning models for automatically classifying news articles by topic. This dataset can be helpful for researchers working on news structuring, classification, and predicting future events based on released news.
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