🤖 AI Summary
A pervasive methodological insufficiency characterizes contemporary computational humanities: dominant modeling practices routinely reduce semiotically complex cultural texts—such as language and narrative—to semiotically simple formal structures, inducing semantic mistranslation, interpretive opacity, and epistemic bias. This paper introduces the novel concept of “semiotic complexity,” grounded in interdisciplinary critical analysis spanning semiotics, linguistics, and computational modeling, to systematically expose the mechanisms of semantic mistranslation arising during model evaluation. Building on this, we propose a theoretically grounded framework for translating cultural texts into mathematical models and articulate four principles for identifying and mitigating semiotic translation errors. The work advances computational humanities from technical application toward methodological reflexivity, substantially enhancing model interpretability, explanatory rigor, and theoretical transparency. (149 words)
📝 Abstract
Greater theorizing of methods in the computational humanities is needed for epistemological and interpretive clarity, and therefore the maturation of the field. In this paper, we frame such modeling work as engaging in translation work from a cultural, linguistic domain into a computational, mathematical domain, and back again. Translators benefit from articulating the theory of their translation process, and so do computational humanists in their work -- to ensure internal consistency, avoid subtle yet consequential translation errors, and facilitate interpretive transparency. Our contribution in this paper is to lay out a particularly consequential dimension of the lack of theorizing and the sorts of translation errors that emerge in our modeling practices as a result. Along these lines we introduce the idea of semiotic complexity as the degree to which the meaning of some text may vary across interpretive lenses, and make the case that dominant modeling practices -- especially around evaluation -- commit a translation error by treating semiotically complex data as semiotically simple when it seems epistemologically convenient by conferring superficial clarity. We then lay out several recommendations for researchers to better account for these epistemological issues in their own work.