A Standardized Project Gutenberg Corpus for Statistical Analysis of Natural Language and Quantitative Linguistics

• Published in: Entropy (Basel, Switzerland) Vol. 22; no. 1; p. 126
• Main Authors: Gerlach, Martin, Font-Clos, Francesc
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 20.01.2020; MDPI
• Subjects: Collaboration; Corpus analysis; Corpus linguistics; Datasets; Information retrieval; jensen–shannon divergence; Language; Linguistics; Machine learning; Metadata; Natural language; Natural language processing; project gutenberg; Quantitative analysis; quantitative linguistics; Reproducibility; Statistical analysis; Tagging; Words (language); Project Gutenberg; natural language processing; Jensen–Shannon divergence; quantitative linguistics; reproducibility
• ISSN: 1099-4300; 1099-4300
• DOI: 10.3390/e22010126

The use of Project Gutenberg (PG) as a text corpus has been extremely popular in statistical analysis of language for more than 25 years. However, in contrast to other major linguistic datasets of similar importance, no consensual full version of PG exists to date. In fact, most PG studies so far either consider only a small number of manually selected books, leading to potential biased subsets, or employ vastly different pre-processing strategies (often specified in insufficient details), raising concerns regarding the reproducibility of published results. In order to address these shortcomings, here we present the Standardized Project Gutenberg Corpus (SPGC), an open science approach to a curated version of the complete PG data containing more than 50,000 books and more than 3 × 10 9 word-tokens. Using different sources of annotated metadata, we not only provide a broad characterization of the content of PG, but also show different examples highlighting the potential of SPGC for investigating language variability across time, subjects, and authors. We publish our methodology in detail, the code to download and process the data, as well as the obtained corpus itself on three different levels of granularity (raw text, timeseries of word tokens, and counts of words). In this way, we provide a reproducible, pre-processed, full-size version of Project Gutenberg as a new scientific resource for corpus linguistics, natural language processing, and information retrieval.