RCV1

RCV1: A New Benchmark Collection for Text Categorization Research. 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.

This software is also peer reviewed by journal TOMS.


References in zbMATH (referenced in 83 articles )

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  1. Bottou, Léon; Curtis, Frank E.; Nocedal, Jorge: Optimization methods for large-scale machine learning (2018)
  2. Francisco Charte, Antonio J. Rivera, David Charte, María J. del Jesus, Francisco Herrera: Tips, guidelines and tools for managing multi-label datasets: the mldr.datasets R package and the Cometa data repository (2018) arXiv
  3. Andrea Esuli, Tiziano Fagni, Alejandro Moreo Fernandez: JaTeCS an open-source JAva TExt Categorization System (2017) arXiv
  4. Du, Rundong; Kuang, Da; Drake, Barry; Park, Haesun: DC-NMF: nonnegative matrix factorization based on divide-and-conquer for fast clustering and topic modeling (2017)
  5. Jalali, Amin; Fazel, Maryam; Xiao, Lin: Variational Gram functions: convex analysis and optimization (2017)
  6. Mania, Horia; Pan, Xinghao; Papailiopoulos, Dimitris; Recht, Benjamin; Ramchandran, Kannan; Jordan, Michael I.: Perturbed iterate analysis for asynchronous stochastic optimization (2017)
  7. Schmidt, Mark; Le Roux, Nicolas; Bach, Francis: Minimizing finite sums with the stochastic average gradient (2017)
  8. Wang, Xiao; Ma, Shiqian; Goldfarb, Donald; Liu, Wei: Stochastic quasi-Newton methods for nonconvex stochastic optimization (2017)
  9. Fountoulakis, Kimon; Gondzio, Jacek: A second-order method for strongly convex $\ell _1$-regularization problems (2016)
  10. Pölitz, Christian; Duivesteijn, Wouter; Morik, Katharina: Interpretable domain adaptation via optimization over the Stiefel manifold (2016)
  11. Abe, Shigeo: Fuzzy support vector machines for multilabel classification (2015)
  12. Huang, Yakui; Liu, Hongwei; Zhou, Sha: An efficient monotone projected Barzilai-Borwein method for nonnegative matrix factorization (2015)
  13. Kuang, Da; Yun, Sangwoon; Park, Haesun: SymNMF: nonnegative low-rank approximation of a similarity matrix for graph clustering (2015)
  14. Le, Tam; Cuturi, Marco: Adaptive Euclidean maps for histograms: generalized Aitchison embeddings (2015)
  15. Lin, Qihang; Lu, Zhaosong; Xiao, Lin: An accelerated randomized proximal coordinate gradient method and its application to regularized empirical risk minimization (2015)
  16. Liu, Ming; Wu, Chong; Chen, Lei: A vector reconstruction based clustering algorithm particularly for large-scale text collection (2015) ioport
  17. Mareček, Jakub; Richtárik, Peter; Takáč, Martin: Distributed block coordinate descent for minimizing partially separable functions (2015)
  18. Chen, Jianhui; Ye, Jieping: Sparse trace norm regularization (2014)
  19. Dudík, Miroslav; Erhan, Dumitru; Langford, John; Li, Lihong: Doubly robust policy evaluation and optimization (2014)
  20. Giatrakos, Nikos; Deligiannakis, Antonios; Garofalakis, Minos; Sharfman, Izchak; Schuster, Assaf: Distributed geometric query monitoring using prediction models (2014)

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