From Corpus to Chronology:

Scalable Readings of Narrative Interviews

Often, numerous interesting topics lie dormant in sources that were originally collected or produced for an entirely different research interest – as in the case of Oral History: the multifaceted content of narrative interviews generally extends far beyond the focus of the original inquiry, thus making them valuable research data for secondary analyses. Within the DFG-funded project Oral-History.Digital, there is a multitude of such interviews from various collections that have been digitized and are accessible online. However, the search for multifaceted and often implicit themes such as migration and home is very complex. The larger the corpus under investigation, the more difficult it becomes to manually access the interviews solely through metadata filters, keyword searches, and transcript viewing. Natural Language Processing techniques, such as Topic Modeling, enable a distant reading to obtain initial information about the sources and the themes they contain, and to use this information for accessing the interviews. This reveals patterns of commonalities and differences in the collections that are barely perceptible to the naked eye.

Also online via Zoom:
https://uni-freiburg.zoom-x.de/j/84893358526?pwd=V0JPUjkwdnlCdzhteUZDS0g0Y2JUUT09

Meeting ID: 848 9335 8526
Passcode: 7k9GBZBX7

The schedule for this semester can be found here:
http://www.hpcl.uni-freiburg.de/index.php?id=598&language=de