Vector hermeneutics: On the interpretation of vector space models of text

Abstract

Scholars working in computational literary studies are increasingly making use of text-derived vector space models, by which I mean numerical models of texts that represent the distribution or modeled relations among the vocabulary extracted from these texts. These models, as this essay will argue, call for distinct modes of humanistic interpretation and explication that are related to but distinct from those that may have been used on the original source texts. While vector space models are analyzed using increasingly complicated quantitative methods and the explanation of their operation requires statistical sophistication, my emphasis on humanistic interpretation is quite intentional. This essay theorizes two major categories of vector space models, the document-term matrix and neural language models, to position these models as not merely descriptions of texts but inscriptive representational objects that perform interpretive work of their own in order to demonstrate the need for a multi-level hermeneutics in computational literary studies.