ONE OF THE most basic questions in literary analysis, the question of the genre of the text being examined, when applied to the study of Old English literature is also one of the most fraught. Though obviously many texts fall clearly into recognized categories, the corpus of Old English literature also contains many examples of texts that blur the lines between genres. The most obvious case is the line between poetry and prose in Old English, which remains the matter of much scholarly debate. Even within those larger genres, however, the boundaries of more specific textual categories can often be difficult to determine, raising hard questions about the way both individual texts and groups of texts may have been understood by Anglo-Saxon authors and readers.

Traditional methods of scholarship can offer some answers to those questions, of course. Textual details—the presence of poetic vocabulary and meter in an Old English poem, the use of traditional sermon structure and direct address to an audience in an Old English homily, for example—usually provide some suggestion as to the intended genre of Old English texts. Manuscript context often offers us further clues to the way an individual text may have been perceived. But especially in cases where such details are not clear, it may be useful to employ digital tools that can examine large groups of texts quickly and perhaps discern patterns and differences that would go unnoticed in a more traditional approach.

Michael Drout's work in statistical analysis of Old English poems, an approach that he has termed “lexomics,” has demonstrated the effectiveness of digital tools in detecting subtle divisions within an individual text as well as lines of influence between one text and another. By examining relative word frequency, Drout's algorithm has successfully recognized the internal division between Genesis A and Genesis B and identified the portion of the Old English poem Daniel that is related to the poem Azarias. Although lexomic methods have been used primarily to identify relationships of sections inside single texts, similar analysis of word frequency has long been used as well to help determine authorship of contested or anonymous works.