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Texts from the HTDL corpus that is are not in the public domain are not available for download, which limits its the usefulness of the corpus for research. However,  a great deal of fruitful research, especially in the form of text mining, can be performed on the basis of non-consumptive reading using extracted features (features extracted from the text) even when the full text is not available. To this end, the HathiTrust Research Center (HTRC) has started making available a set of page-level features extracted from the HTDL's public domain volumes. These extracted features can be the basis for certain kinds of  algorithmic analysis. For example, topic modeling works with bags of words (sets of tokens). Since tokens and their frequencies are provided as features, the EF dataset can enable a user to perform topic modeling with the data.

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