Bioinformatics and Classical Literary Study

TL;DR

This paper introduces the Quantitative Criticism Lab, which applies computational biology, NLP, and machine learning to literary analysis, focusing on authorial style and intertextuality, exemplified through sequence alignment in Latin literature.

Contribution

It presents a novel interdisciplinary approach combining sciences and humanities, demonstrating the use of sequence alignment for detecting intertextuality in Latin texts.

Findings

Sequence alignment effectively detects phonetic echoes in Latin literature.

The approach can be extended to other languages and corpora.

Interdisciplinary methods enhance literary analysis.

Abstract

This paper describes the Quantitative Criticism Lab, a collaborative initiative between classicists, quantitative biologists, and computer scientists to apply ideas and methods drawn from the sciences to the study of literature. A core goal of the project is the use of computational biology, natural language processing, and machine learning techniques to investigate authorial style, intertextuality, and related phenomena of literary significance. As a case study in our approach, here we review the use of sequence alignment, a common technique in genomics and computational linguistics, to detect intertextuality in Latin literature. Sequence alignment is distinguished by its ability to find inexact verbal similarities, which makes it ideal for identifying phonetic echoes in large corpora of Latin texts. Although especially suited to Latin, sequence alignment in principle can be extended to many other languages.