Linking Literature, Bioinformatics, and Machine Learning through the Quantitative Criticism Lab
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Abstract
Principal Investigator: Pramit Chaudhuri, University of Texas at Austin; Co-Principal Investigator: Joseph P. Dexter, Harvard University. The Quantitative Criticism Lab (QCL) is producing a web-based suite of tools for traditionally-trained humanists to analyze literary texts in a quantitative manner. The tools are designed with an important class of literary problems in mind, exemplified by the identification of verbal parallels and, at a larger scale, by the individuating of entire works within generic traditions. The two main computational approaches involved are sequence alignment for the detection of verbal resemblance, and stylometry augmented by machine learning for the profiling of texts and corpora. QCL’s research includes enhancement of an existing sequence alignment tool for Latin (Fīlum) and its extension to ancient Greek, Italian, and English, and the leveraging of previous work on Latin style to create a user-friendly stylometry toolkit applicable to multiple premodern languages. Partners will include faculty members and students from Austin Community College, Trinity University, and Rice University.