BBC Datasets - Machine Learning + Network Analysis Resources

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BBC Datasets

Two news article datasets, originating from BBC News, provided for use as benchmarks for machine learning research. These datasets are made available for non-commercial and research purposes only. If you make use of these datasets please consider citing the publication: D. Greene and P. Cunningham. "Practical Solutions to the Problem of Diagonal Dominance in Kernel Document Clustering", Proc. ICML 2006. [PDF] [BibTeX].

Dataset: BBC

All rights, including copyright, in the content of the original articles are owned by the BBC.

  • Consists of 2225 documents from the BBC news website corresponding to stories in five topical areas from 2004-2005.
  • Class Labels: 5 (business, entertainment, politics, sport, tech)

>> Download pre-processed dataset

>> Download raw text files

Dataset: BBCSport

All rights, including copyright, in the content of the original articles are owned by the BBC.

  • Consists of 737 documents from the BBC Sport website corresponding to sports news articles in five topical areas from 2004-2005.
  • Class Labels: 5 (athletics, cricket, football, rugby, tennis)

>> Download pre-processed dataset

>> Download raw text files

File formats

The datasets have been pre-processed as follows: stemming (Porter algorithm), stop-word removal (stop word list) and low term frequency filtering (count < 3) have already been applied to the data. The files contained in the archives given above have the following formats:

  • *.mtx: Original term frequencies stored in a sparse data matrix in Matrix Market format.
  • *.terms: List of content-bearing terms in the corpus, with each line corresponding to a row of the sparse data matrix.
  • *.docs: List of document identifiers, with each line corresponding to a column of the sparse data matrix.
  • *.classes: Assignment of documents to natural classes, with each line corresponding to a document.
  • *.urls: Links to original articles, where appropriate.

Contact

For further information please contact Derek Greene.

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