RCV1: A New Benchmark Collection for Text Categorization Research
RCV1: новая эталонная коллекция для исследований категоризации текстов
2004-12-01
SCID: 54.1/mvww7ntj
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RCV1 text categorization benchmarkReuters Corpus Volume Ihierarchical category taxonomiesnewswire categorizationsupervised learning methods
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Abstract (AI)
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.
Key Findings
1
Detailed per-category experimental results and corrected data resources are provided through online appendices to support future research.
2
Error correction produces RCV1-v2 from the original RCV1-v1, including corrected category assignments and taxonomy structures.
3
RCV1 provides a benchmark collection of over 800,000 manually categorized Reuters newswire stories for text categorization research.
4
Several widely used supervised learning methods are benchmarked on RCV1-v2, establishing baseline results and revealing the collection’s properties.
5
The paper documents RCV1’s real-world coding policy, quality-control procedures, and intended semantics of its hierarchical category taxonomies.
Research Object
RCV1-v2, a corrected archive of over 800,000 manually categorized Reuters newswire stories with hierarchical category taxonomies
Research Subject
The properties of the collection and the baseline performance of supervised text-categorization methods under its real-world coding and quality constraints
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2004-12-01
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