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    Comparing “parallel passages” in digital archives

    Harris, Martyn and Levene, Mark and Zhang, Dell and Levene, D. (2019) Comparing “parallel passages” in digital archives. Journal of Documentation , ISSN 0022-0418. (In Press)

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    Abstract

    Purpose: The purpose of this paper is to present a language-agnostic approach to facilitate the discovery of “parallel passages” stored in historic and cultural heritage digital archives. Design/methodology/approach: The authors explore a novel, and relatively simple approach, using a character-based statistical language model combined with a tailored version of the Basic Local Alignment Tool to extract exact and approximate string patterns shared between groups of documents. Findings: The approach is applicable to a wide range of languages, and compensates for variability in the text of the documents as a result of differences in dialect, authorship, language change over time and errors due to inaccurate transcriptions and optical character recognition errors as a result of the digitisation process. Research limitations/implications: A number of case studies demonstrate that the approach is practical and generalisable to a wide range of archives with documents in different languages, domains and of varying quality. Practical implications: The approach described can be applied to any digital archive of modern and contemporary texts. This makes the approach applicable to digital archives recording historic texts, but also those composed of more recent news articles, for example. Social implications: The analysis of “parallel passages” enables researchers to quantify the presence and extent of text-reuse in a collection of documents, which can provide useful data on author style, text genres and cultural contexts. Originality/value: The approach is novel and addresses a need by humanities researchers for tools that can identify similar documents and local similarities represented by shared text sequences in a potentially vast large archive of documents. As far as the authors are aware, there are no tools currently exist that provide the same level of tolerance to the language of the documents.

    Metadata

    Item Type: Article
    Keyword(s) / Subject(s): Digital Archives, Information Retrieval, Statistical Language, Models, Suffixes, Trees
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Administrator
    Date Deposited: 22 Jul 2019 09:56
    Last Modified: 16 Sep 2019 21:21
    URI: http://eprints.bbk.ac.uk/id/eprint/28183

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