Page-level features from 15.7 million volumes

Note that this data release has been superseded by the HTRC Extract Features Dataset v.2.0, which contains data for 17.1 million volumes

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 15.7 million volumes of the HathiTrust Digital Library.

Features are notable or informative characteristics of the text. 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 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.

Downloading the files

See directions for Ecxtracted Features v.1.5

Attribution

Boris Capitanu, Ted Underwood, Peter Organisciak, Timothy Cole, Maria Janina Sarol, J. Stephen Downie (2016). The HathiTrust Research Center Extracted Feature Dataset (1.0) [Dataset]. HathiTrust Research Center, http://dx.doi.org/10.13012/J8X63JT3.

This feature dataset is released under a Creative Commons Attribution 4.0 International License.

This is the full release (Nov 2016). The earlier, public-domain subset (Spring 2015) is still available.

Data Stats

# of volumes	15,722,079
# of pages	5,787,519,444
# of tokens	2,449,739,213,773
# of IC volumes	9,914,509
# of IC pages	3,005,448,348
# of IC tokens	1,777,793,828,310
# of PD volumes	5,807,570
# pd pages	2,602,212,586
# pd tokens	1,197,838,539,662

Feature File Documentation

The HTRC Extracted Features provides a small amount of metadata in addition to the quantitative features.

Metadata

A significant amount of bibliographic metadata for identifying the volume is included in this dataset. See also: "Where can I find detailed bibliographic metadata?".

volumeIdentifier: A unique identifier for the current volume. This is the same identifier used in the HathiTrust and HathiTrust Research Center corpora.

schemaVersion: A version identifier for the format and structure of this metadata object. metadata.schemaVersion is separate from features.schemaVersion below.

dateCreated: The time this metadata object was processed. metadata.dateCreated is not necessarily the same as the features.dataCreated below.

accessProfile: The original source of a volume.

title: Title of the volume.

pubDate: The publication year.

pubPlace: The publication location.

language: The primary language of the volume.

genre: The genre of the volume.

issuance: The bibliographic level of a volume

typeOfResource: The format type of a volume.

names: The personal and corporate names associated with a volume.

imprint: The place of publication, publisher, and publication date of the given volume.

enumerationChronology: Contains issue-specific information in unstructured form (number, publication date)

governmentDocument: Denotes if a volume is a government document.

rightsAttributes: The rights attributes for a volume.

hathitrustRecordNumber: The unique record number for the volume in the HathiTrust Digital Library.

htBibUrl: The HathiTrust Bibliographic API call for the volume.

handleUrl: The persistent identifier for the given volume.

sourceInstitution: The original institution who contributed the volume.

sourceInsitutionRecordNumber: The unique record number for the volume from its original institution.

oclc: The array of OCLC number(s).

isbn: The International Standard Book Number for a volume.

issn: The International Standard Serial Number for a volume.

lccn: The Library of Congress Call Number for a volume.

classification: The call number supplied by the originating library.

bibliographicFormat: The format of a volume's bibliography.

lastUpdatePage: The date this page was last updated.

Features

Volume-Level Features

schemaVersion: A version identifier for the format and structure of the feature data (HTRC generated).

dateCreated: The time the batch of metadata was processed and recorded (HTRC generated).

pageCount: The number of pages in the volume.

pages: An array of JSON objects, each representing a page of the volume.

Page-Level Features

Pages are contained within volumes, they have a sequence number, and information about their header, body, and footer.

Page-level information

seq: The sequence number. More details on this value.

tokenCount: The total number of tokens on the page.

lineCount: The total number of non-empty lines on the page.

emptyLineCount: The total number of empty lines on the page.

sentenceCount: Total number of sentences identified on the page using OpenNLP. Details on parsing.

languages: Automatically inferred language likelihood for the page, Shuyo Nakatani's Language Detection library. Language code reference.

Header, Body, and Footer information

The fields for header, body, and footer are the same, but pertain to different parts of the page. Read about the differences between the sections.

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.

beginCharCounts: Aggregated frequency counts of the first non-whitespace character on each line. (Renamed in v.1, previously beginLineChars).

endCharCount: Count of the last character on each line in this page section (ignoring whitespace). (Renamed in v.1, previously endLineChars).

capAlphaSeq: The longest length of the alphabetical sequence of capital characters starting a line. (Body only).

Acknowledgements

This research was supported in part by Lilly Endowment, Inc., through its support for the Indiana University Pervasive Technology Institute, and in part by the Indiana METACyt Initiative. The Indiana METACyt Initiative at IU is also supported in part by Lilly Endowment, Inc.

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.

Questions

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.

OpenNLP uses the Penn Treebank POS tags.

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.

Extracted Features 2.0 includes a hash that can help track when page content has changed.

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: extracted from MARC field 260, with MARC 260 subfield $a pulled from the HathiTrust MARC record, and 260 $b and 260 $c from Hathifiles.
language: extracted from MARC control field 008 from Hathifiles.
pubDate: extracted from Hathifiles. See also: details on HathiTrust's rights-determination.
oclc: OCLC number extracted 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.

Known Issues

The current EF dataset has two known issues:

Chinese and Japanese tokens sometimes have a zero-width space character, \u200b, at the start or end of the token.

This issue is fixed in Extracted Features 2.0.

The character count information is “beginCharCounts” and “endCharCount”

This issue is fixed in Extracted Features 2.0.

Has a fix been released yet? No. Watch this space or adapt your code.

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