Methods for Extracting Relational Data from Unstructured Texts Prior to Network Visualization in Humanities Research. (19th November 2020)

• Record Type: Journal Article
• Authors: Graham, S. Scott; Majdik, Zoltan P.; Clark, Dave
• Is Part Of: Journal of open humanities data. Volume 6(2020)
• Publication Date: 2020-11-19
• Subjects: bioethics -- medical humanities -- network modelling -- natural language processing -- machine learning; Humanities -- Periodicals; 001.3
• DOI: 10.5334/johd.21
• Languages: English
• ISSNs: 2059-481X

Abstract

Network modelling methodologies in the digital humanities have been be used to advance inquiry in a variety of areas—most commonly those having to do with correspondence, citation, and social media networks. While new technologies have made generating high-quality and even dynamic network visualizations relatively easy, key challenges remain for humanities researchers. Many common objects of humanistic inquiry, such as literary, historiographic, and biographical texts are often not easily transformed into the kinds of data structures necessary for network visualization. The Transparency to Visibility (T2V) Project was initiated to develop new methods and toolkits that can support humanistic researchers who need to extract relationship data from unstructured texts to support network visualization. The T2V team used bioethics accountability statements to pilot and evaluate different methods for extracting relationship data. The resulting machine-learning-enhanced natural language processing (NLP) and metadata-assisted approaches offer promising potential pathways for contemporary digital humanities and future toolkit development.