Access to Billions of Pages for Large-Scale Text Analysis

Organisciak, Peter; Capitanu, Boris; Underwood, Ted; Downie, J. Stephen

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https://hdl.handle.net/2142/98873 Copy

Description

Title

Access to Billions of Pages for Large-Scale Text Analysis

Author(s)

• Organisciak, Peter
• Capitanu, Boris
• Underwood, Ted
• Downie, J. Stephen

Issue Date

2017

Keyword(s)

• Non-consumptive research
• Feature extraction
• Large-scale text analysis
• Datasets
• Text mining

Abstract

Consortial collections have led to unprecedented scales of digitized corpora, but the insights that they enable are hampered by the complexities of access, particularly to in-copyright or orphan works. Pursuing a principle of non-consumptive access, we developed the Extracted Features (EF) dataset, a dataset of quantitative counts for every page of nearly 5 million scanned books. The EF includes unigram counts, part of speech tagging, header and footer extraction, counts of characters at both sides of the page, and more. Distributing book data with features already extracted saves resource costs associated with large-scale text use, improves the reproducibility of research done on the dataset, and opens the door to datasets on copyrighted books. We describe the coverage of the dataset and demonstrate its useful application through duplicate book alignment and identification of their cleanest scans, topic modeling, word list expansion, and multifaceted visualization.

Publisher

iSchools

Series/Report Name or Number

iConference 2017 Proceedings Vol. 2

Type of Resource

text

Language

en

Permalink

http://hdl.handle.net/2142/98873

Copyright and License Information

Copyright 2017 is held by the authors.

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