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SUMMARY:Exploring Enlightenment: Text Mining and Intertextuality in the Ei
 ghteenth Century Republic of Letters
DTSTART:20121108T101500
DTSTAMP:20260406T112104Z
UID:64b56517d6c389b621886f262751c5087af26c839c07719e90731704
CATEGORIES:Miscellaneous
DESCRIPTION:Glenn Roe\, Mellon Fellow in Digital Humanities\, University o
 f Oxford\nThe challenge of ‘Big Data’ in the Humanities has led in rec
 ent years to a host of innovative technological and algorithmic approaches
  to the growing digital human record. These techniques—from data mining 
 to distant reading—can offer students and scholars new perspectives on t
 he exploration and visualisation of increasingly intractable data sets in 
 the human and social sciences\; perspectives that would have previously be
 en unimaginable. The danger\, however\, in these kinds of ‘macro-analyse
 s’\, is that scholars find themselves increasingly disconnected from the
  raw materials of their research\, engaging with massive collections of te
 xts in ways that are neither intuitive nor transparent\, and that provide 
 few opportunities to apply traditional modes of ‘close reading’ to the
 se new resources. In this talk\, I will outline some of my previous work u
 sing data mining and machine learning techniques to explore large data set
 s drawn primarily from the French Enlightenment period. Building upon thes
 e past experiences\, I will then present my current research project at Ox
 ford\, which uses sequence alignment algorithms to identify intertextual r
 elationships between authors and texts in the 18th-century “Republic of 
 Letters.” By reintroducing the notion of (inter)textuality into algorith
 mic and data-driven methods of ‘macro-anlalysis’ we can perhaps bridge
  the gap between distant and close readings\, by way of an intermediary mo
 de of scholarship I term ‘directed’ or ‘scalable’ reading.
LOCATION:BC 02 https://plan.epfl.ch/?room==BC%2002
STATUS:CONFIRMED
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