A great deal of useful research can be performed non-consumptively with pre-extracted features. For this reason, we've prepared a data export of features for the public domain volumes of the HathiTrust Digital Library.
Features are notable or informative characteristics of the text. Also, we have processed a number of useful features, including part-of-speech tagged token counts, header and footer identification, and various line-level information. These are provided per-page. Providing token information at the page-level makes it possible to separate text from paratext; for instance, a researcher may use the information to identify publishers' ads at the back of a book. For cleaner text, headers and footers are also identified distinctly from page content. The specific features that we extract for each page are described in more detail below.
The most useful extracted feature that we are provide is the token (unigram) count, on a per-page basis. Term counts are specific to the part-of-speech usage for that term, so that a term used as both a noun and a verb, for example, will have separate counts provided for both these modalities of its use. We also include line information, such as the number of lines with text on each page, and a count of characters that start and end lines on each page. This information can illuminate genre and volume structure: for instance, it helps distinguish poetry from prose, or body text from an index.
Boris Capitanu, Ted Underwood, Peter Organisciak, Sayan Bhattacharyya, Loretta Auvil, Colleen Fallaw, J. Stephen Downie (2015). Extracted Feature Dataset from 4.8 Million HathiTrust Digital Library Public Domain Volumes (0.2)[Dataset]. HathiTrust Research Center, http://dx.doi.org/10.13012/j8td9v7m.
tokenCount: The total number of tokens in this page section.
lineCount: The number of lines containing characters of any kind in this page section. This pertains to the layout of the page; for sentence counts, see the sentenceCount field.
emptyLineCount: The number of lines without text in this page section.
sentenceCount: The number of sentences found in the text in this page section, parsed using OpenNLP.
tokenPosCount: An unordered list of all tokens (characterized by part of speech using OpenNLP), and their corresponding frequency counts, in this page section. Tokens are case-sensitive, so a capitalized "Rose" is shown as a separate token. There will be separate counts, for instance, for "rose" (noun) and "rose" (verb). Words separated by a hyphen across a line break are rejoined. No other data cleaning or OCR correction was performed. Details on POS parsing and types of tags used.
beginLineChars: Aggregated frequency counts of the first non-whitespace character on each line.
endLineChars: Count of the last character on each line in this page section (ignoring whitespace).
capAlphaSeq: The longest length of the alphabetical sequence of capital characters starting a line. (Body only).
imprint: The place of publication, publisher, and publication date of the given volume.
Computation was supported by the Cline Center for Democracy through its allocation on the Blue Waters sustained-petascale computing project, which is funded by the National Science Foundation (awards OCI-0725070 and ACI-1238993) and the state of Illinois. Blue Waters is a joint effort of the University of Illinois at Urbana-Champaign and its National Center for Supercomputing Applications.
This release has been made possible, in part, by the National Endowment for the Humanities: Celebrating 50 Years of Excellence. Any views, findings, conclusions, or recommendations expressed in this release do not necessarily represent those of the National Endowment for the Humanities.
How are tokens parsed?
Hyphenation of tokens at end of line was corrected using custom code. Apache OpenNLP was used for sentence segmentation, tokenization, and part of speech (POS) tagging. No additional data cleaning or OCR correction was performed.
Can I use the page sequence as a unique identifier?
The seq value is always sequential from the start. Each scanned page of a volume has a unique sequence number, but it is specific to the current version of the full text. In theory, updates to the OCR that add or remove pages will change the sequence. The practical likelihood of changes in the sequence is low, but uses of the page as an id should be cautious.
A future release of this data will include persistent page identifiers that remain unchanged even when page sequence changes.
Where is the bibliographic metadata? Who wrote the book?; When was it published, etc.?
This dataset is foremost an extracted features dataset, with minimal metadata included as a convenience. For additional metadata information, i.e. subject classifications, etc., HT offers Hathifiles, which can be paired to our feature dataset through the volume id field.
The metadata that is included in this data includes MARC metadata from HathiTrust and additional information from Hathifiles:
imprint: 260a from HathiTrust MARC record, 260b and 260c from Hathifiles.
Additionally, schemaVersion and dateCreated are specific to this feature dataset.
What do I do with beginning- or end-of-line characters?
The characters at the start and end of a line can be used to differentiate text from paratext at a page level. For instance, index lines tend to begin with capitalized letters and end with numbers. Likewise, lines in a table of contents can be identified through arabic or roman numerals at the start of a line.
What is the difference between the header, body, and footer sections?
Because repeated headers and footers can distort word counts in a document, but also help identify document parts, we attempt to identify repeated lines at the top or bottom of a page and provide separate token counts for those forms of paratext. The "header" and "footer" sections will also include tokens that are page numbers, catchwords, or other short lines at the very top or bottom of a page. Users can of course ignore these divisions by aggregating the token counts for header, body, and footer sections.