This is a follow-up to my earlier post on Topic Models and Spelling Variation: The Case of Early Middle English. There I discussed the challenges of generating topic models of texts with non-standardised spelling where topics did not merely correspond to texts. In any given model, differences in the spelling systems of two texts will cause the generated topics to be prominent in some texts and effectively non-existent in others, whereas other topics will be non-existent in the former and prominent in the latter. Topics are thus essentially orthographic patterns, rather than rhetorical discourses or indicators of subject matter. My initial experiments showed that some linguistic smoothing–admittedly, a questionable form of textual deformance–can help address this problem, along with increasing the granularity of the model by breaking texts into small chunks. Chunk sizes of about 1000 words began to reveal the sorts of patterns I was looking for: cases where individual topics had high prominence in parts of more than one text. But I speculated that chunk sizes needed to be still smaller in order make these patterns more noticeable. A second reason for using smaller chunk sizes is that it becomes much easier to make the leap back from distant to close reading.… Read more…
Month: January 2013
Topic Models and Spelling Variation: The Case of Early Middle English
Topic Modelling has developed quite a following in the DH world, but it still has a long way to go before it proves itself a reliable method for literary research. (Caveat: I have not yet read Matthew Jockers’ soon-to-be released Macroanalysis, which may answer many questions about how to use topic modelling to study literature.) As far as I can tell, topic modelling was originally tested on materials that, although diverse in subject matter, were fairly homogeneous in language. Literary language is problematic for topic modelling not so much because it contains more ambiguities or fuzziness than, say, scientific journals but because the types of questions literary scholars ask tend to probe at these aspects of language. There’s no reason why we should expect a single, and fairly new, computational method to provide miraculous insight into questions that sustain whole disciplinary fields, and neither is that a reason to assume that it can provide no insight at all. Topic modelling has already shown particular importance in the area of literary history, as can be seen from the work of Jockers’s work, as well as that of people like Ted Underwood and Lisa Rhody. But the results that they have made available share one thing in common with the various topic models of scientific journals, Day of DH posts, PMLA articles, and the like.… Read more…