Pramit Chaudhuri F'18, F'15
ACLS Digital Extension Grants 2018
University of Texas at Austin
Linking Literature, Bioinformatics, and Machine Learning through the Quantitative Criticism Lab
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.
ACLS Digital Innovation Fellowships 2015
Computational Analysis of Intertextuality in Classical Literature
Literary scholarship has long been preoccupied with identifying verbal relations among texts (“intertextuality”). This project is developing a suite of new computational tools to enable researchers to trace connections among Latin and Greek texts at a higher order of scale and efficiency than manual searches: 1) a sequence alignment tool that identifies verbal parallels that are close but inexact (the commonest kind of intertextuality); 2) a digital Greek-Latin thesaurus to enable identification of parallels across languages by virtue of their meaning; 3) a pattern recognition tool that detects passages of similar metrical structure; and 4) a set of tools designed for classification of texts according to various stylistic metrics, which will be useful for studies of quotation and attribution.