Using NLP to Support Dynamic Arrangement, Description, and Discovery of Born Digital Collections: The ArchExtract Experiment

By Mary W. Elings

This post is the eighth in our Spring 2016 series on processing digital materials.

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Many of us working with archival materials are looking for tools and methods to support arrangement, description, and discovery of electronic records and born digital collections, as well as large bodies of digitized text. Natural Language Processing (NLP), which uses algorithms and mathematical models to process natural language, offers a variety of potential solutions to support this work. Several efforts have investigated using NLP solutions for analyzing archival materials, including TOME (Interactive TOpic Model and MEtadata Visualization), Ed Summers’ Fondz, and Thomas Padilla’s Woese Collection work, among others, though none have resulted in a major tool for broader use.

One of these projects, ArchExtract, was carried out at UC Berkeley’s Bancroft Library in 2014-2015. ArchExtract sought to apply several NLP tools and methods to large digital text collections and build a web application that would package these largely command-line NLP tools into an interface that would make it easy for archivists and researchers to use.

The ArchExtract project focused on facilitating analysis of the content and, via that analysis, discovery by researchers. The development work was done by an intern from the UC Berkeley School of Information, Janine Heiser, who built a web application that implements several NLP tools, including Topic Modelling, Named Entity Recognition, and Keyword Extraction to explore and present large, text-based digital collections.

The ArchExtract application extracts topics, named entities (people, places, subjects, dates, etc.), and keywords from a given collection. The application automates, implements, and extends various natural language processing software tools, such as MALLET and the Stanford Core NLP toolkit, and provides a graphical user interface designed for non-technical users.

 

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ArchExtract Interface Showing Topic Model Results. Elings/Heiser, 2015.

In testing the application, we found the automated text analysis tools in ArchExtract were successful in identifying major topics, as well as names, dates, and places found in the text, and their frequency, thereby giving archivists an understanding of the scope and content of a collection as part of the arrangement and description process. We called this process “dynamic arrangement and description,” as materials can be re-arranged using different text processing settings so that archivists can look critically at the collection without changing the physical or virtual arrangement.

The topic models, in particular, surfaced documents that may have been related to a topic but did not contain a specific keyword or entity. The process was akin to the sort of serendipity a researcher might achieve when shelf reading in the analog world, wherein you might find what you seek without knowing it was there. And while topic modelling has been criticized for being inexact, it can be “immensely powerful for browsing and isolating results in thousands or millions of uncatalogued texts” (Schmidt, 2012). This, combined with the named entity and keyword extraction, can give archivists and researchers important data that could be used in describing and discovering material.

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ArchExtract Interface Showing Named Entity Recognition Results. Elings/Heiser, 2015.

As a demonstration project, ArchExtract was successful in achieving our goals. The code developed is documented and freely available on GitHub to anyone interested in how it was done or who might wish to take it further. We are very excited by the potential of these tools in dynamically arranging and describing large, text-based digital collections, but even more so by their application in discovery. We are particularly pleased that broad, open source projects like BitCurator and ePADD are taking this work forward and will be bringing NLP tools into environments that we can all take advantage of in processing and providing access to our born digital materials.

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Mary W. Elings is the Principal Archivist for Digital Collections and Head of the Digital Collections Unit of The Bancroft Library at the University of California, Berkeley. She is responsible for all aspects of the digital collections, including managing digital curation activities, the born digital archives program, web archiving, digital processing, mass digitization, finding aid publication and maintenance, metadata, archival information management and digital asset management, and digital initiatives. Her current work concentrates on issues surrounding born-digital materials, supporting digital humanities and digital social sciences, and research data management. Ms. Elings co-authored the article “Metadata for All: Descriptive Standards and Metadata Sharing across Libraries, Archives and Museums,” and wrote a primer on linked data for LAMs. She has taught as an adjunct professor in the School of Information Studies at Syracuse University, New York (2003-2009) and School of Library and Information Science, Catholic University, Washington, DC (2010-2014), and is a regular guest-lecturer in the John F. Kennedy University Museum Studies program (2010-present).

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