Knowra Digital humanities Digital humanities Digital humanities is an interdisciplinary field that uses computational methods and digital tools to study, preserve, and present humanities materials and questions.
Digital edition : A scholarly presentation of a text that uses digital technologies for transcription, annotation, search, or comparison. Digital editions combine humanities interpretation with searchable, structured presentation of texts.
Corpus linguistics : The study of language through principled analysis of large collections of texts. Its corpus-based methods inform digital analysis of literary and historical language.
Roberto Busa : An Italian Jesuit scholar who pioneered computer-assisted analysis of the writings of Thomas Aquinas. His collaboration on the Index Thomisticus is often treated as an early landmark of digital humanities.
Distant reading : The analysis of literary patterns across many texts rather than detailed interpretation of individual works. It contrasts with close reading while often relying on digital-scale textual analysis.
Digital archive : A digital collection that preserves and provides access to records, documents, or cultural objects. These archives make dispersed or fragile humanities materials available for study.
Natural language processing : Computational methods for analyzing, understanding, or generating human language. NLP supplies tools for classifying, searching, and comparing humanities texts.
Index Thomisticus : A concordance and lemmatized index of Thomas Aquinas’s works, produced with computer assistance. The project demonstrated large-scale computational methods for studying a major textual corpus.
Close reading : The careful interpretation of a text’s language, form, and structure in detail. It offers a different scale of evidence from computational analysis across large collections.
Text mining : The computational extraction of patterns and information from textual data. It lets scholars examine large bodies of writing beyond close reading alone.
Topic model : A statistical model that infers recurring clusters of words across a collection of documents. Topic models can suggest patterns in large textual collections for later interpretation.
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