Projects

Reading Early Modern Irish: a Digital Guide to Irish Gaelic (c.1200-1650)

While there are many resources available for learning Modern and Old Irish, there are no comparable materials for learning Early Modern Irish, the written form of the language used from roughly 1200-1650.  There is no comprehensive grammar, no guide to translation and interpretation, and no dictionary.  Consequently, nearly 500 years of Irish writing remains underused by scholars as the difficulty of acquiring the language limits access to a small group of specialists.  The “Reading Early Modern Irish” project addresses this scholarly gap by offering the first systematic introductory apparatus for learning to read, transcribe and translate Early Modern Irish.

This is a multi-institutional, collaborative project that has received startup funding from both Notre Dame and UCONN.  Beginning in the summer of 2014, historian Brendan Kane and I have been working with Tom Scheinfeldt on grant applications that would fund the project on an ongoing basis.

 

Topic Modeling the Poems of Anne Finch (1661-1720)

In 2013 I published an article entitled “Trees in Anne Finch’s Jacobite Poems of Retreat” (SEL 53.3 [2013]: 541-563).  Looking over Finch’s corpus of poetry, I had noticed recurring images of trees, shade, sunlight and winds.  These images, as I argue in the article, are closely related to Finch’s political outlook as a Jacobite/non-juror.  Shortly after the article was published, however, I became interested in using certain DH techniques in my work.  I began to wonder if any of the various topic modeling algorithms could capture this aspect of Finch’s poetry, which I had discovered through traditional close reading.

Still a work in progress (I made some mistakes early on in how I split the poems into separate text files), I am using the MALLET software package from UMASS Amherst for this project.  The goals of the project are three-fold.  First, can topic modeling reproduce results similar to results that are achieved through ordinary close reading?  Second, will topic modeling reveal insights that had been overlooked when using more conventional methods of textual analysis?  And third, is it possible to achieve satisfactory results on a relatively small corpus of texts — in this case, poems?