A nice, talk-length summary of the MONK Project‘s goals, methods, and initial use cases by John Unsworth, the co-PI. A useful place to direct people who wonder what digital literary studies might be about, if one doesn’t just want to dump them into Literary and Linguistic Computing or one of the many recent monographs/anthologies.
One note: John closes with a brief discussion of Brad Pasanek and D. Sculley’s recent piece, “Meaning and mining: The impact of implicit assumptions in data mining for the humanities,” in LLC, which is a kind of cautionary tale about the (fundamental, inescapable) role of interpretation in computationally assisted literary criticism. Pasanek and Sculley are right, of course, that computational results require close reading of their own, and there are probably people who sorely need this reminder—in fact there are probably a whole lot of humanities scholars who do—but I don’t think this group has much overlap with the set of people doing actual quantitative work. If there’s one thing we’ve learned from science studies—which is, after all, the sociological and theoretical study of fields that are grounded overwhelmingly in quantitative methods—it’s that experiments alone don’t tell us anything, much less give us unmediated access to objective truth. Pasanek and Sculley do a nice and valuable job of illustrating some specific issues in digital humanities, but I think the take-home message is “Remember Latour! (Or Kuhn! Or Fleck! Or Bloor! Or Shapin! Or …!)”
[Update: The original version of this post linked to the wrong Pasanek and Sculley article. I’ve corrected the link above; the erroneous one (well worth a read in its own right) was “Mining Millions of Metaphors.”]