Name
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. |
Year | Venue | Keywords |
|---|---|---|
2004 | Journal of Machine Learning Research | feature selection,errorful data,scutfbr,evaluation,original data,test collection,k-nn,support vector machines,text classification,news articles,applications,multilabel,nearest neighbor,scut,category assignment,rcv1 data,text categorization research,automated indexing,term weighting,reuters documentation,operational systems,rocchio,corrected data,new benchmark collection,methodology,thresholding,research purpose,svms,reuters personnel,multiclass,controlled vocabulary indexing,detailed understanding,reuters corpus volume,effectiveness mea- sures,support vector machine,indexation,operating system,controlled vocabulary,quality control,supervised learning |
Field | DocType | Volume |
Information retrieval,Pattern recognition,Computer science,Supervised learning,Coding (social sciences),Quality control,Artificial intelligence,Text categorization,Documentation,Semantics,Machine learning | Journal | 5, |
Issue | ISSN | Citations |
1 | 1532-4435 | 687 |
PageRank | References | Authors |
59.07 | 28 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| David D. Lewis | 1 | 4560 | 737.43 |
| Yiming Yang | 2 | 3299 | 344.91 |
| Tony G. Rose | 3 | 706 | 64.38 |
| Fan Li | 4 | 687 | 59.07 |
| DD Lewis | 5 | 687 | 59.07 |
| YM Yang | 6 | 687 | 59.07 |
| TG Rose | 7 | 687 | 59.07 |