RCV1: A New Benchmark Collection for Text Categorization Research

TLDR

Reuters Corpus Volume I (RCV1) is an archive of over 800,000 manually categorized newswire stories made available for research, and using it requires understanding the real‑world constraints under which the data were produced. The authors benchmark several widely used supervised learning methods on the cleaned RCV1‑v2 collection to illustrate its properties and suggest new research directions. They describe Reuters’ coding policy, quality‑control procedures, taxonomy semantics, and the corrections applied, defining the original RCV1‑v1 and the corrected RCV1‑v2 dataset. Baseline per‑category experimental results, corrected category assignments, and taxonomy structures are provided in online appendices for future studies.

Abstract

Reuters Corpus Volume I (RCV1) is an archive of over 800,000 manually categorized newswire stories recently made available by Reuters, Ltd. for research purposes. Use of this data for research on text categorization requires a detailed understanding of the real world constraints under which the data was produced. Drawing on interviews with Reuters personnel and access to Reuters documentation, we describe the coding policy and quality control procedures used in producing the RCV1 data, the intended semantics of the hierarchical category taxonomies, and the corrections necessary to remove errorful data. We refer to the original data as RCV1-v1, and the corrected data as RCV1-v2. We benchmark several widely used supervised learning methods on RCV1-v2, illustrating the collection's properties, suggesting new directions for research, and providing baseline results for future studies. We make available detailed, per-category experimental results, as well as corrected versions of the category assignments and taxonomy structures, via online appendices.