Labeled datasets are essential for text categorization. They are used to train a classifier, or as a benchmark collection to evaluate categorization algorithms. However, labeling a large-scale document set is extremely expensive because it involves much human labour, and the labeling process itself is subjective rather than objective. Therefore, labels assigned to documents by only one human editor in some existing labeled document sets may be of limited use and may prove problematic for training a classifier or evaluating categorization algorithms. This research explores socially constructed Web directory, the Open Directory Project (ODP), to generate a series of labeled document sets by extracting semantic characteristics from the ODP categories which are annotated by a list of indexed Websites. The generated document sets are used to classify Web search results and the results are encouraging.
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