When you think of computer-enabled text analysis, do you think of TEI? Whether your answer is yes or no, there’s a reason. TEI (Text Encoding Initiative) mark-up language may be one of the oldest, most widely adopted, flexible and standardized approaches to analyzing text-based materials. It’s also labor-intensive, and difficult (if not impossible) to scale to really large sets of texts. Is it the anti-thesis of text-mining? What’s it’s value for researchers who need to read texts in different ways?
In the next installment of the Text > Data Digital Scholarship series, Will Shaw (UNC graduate student and Duke University Libraries/Humanities Writ Large digital humanities consultant) raises TEI to new prominence. He’ll highlight what TEI offers to text analysis and showcase projects — from multi-year, international DH projects to new endeavors at Duke — that are re-discovering its potential. Join us Thursday, September 27, 2:00-3:30 PM, in Perkins Library 217. Register to attend: http://library.duke.edu/events/digital-scholarship/event.do?id=6331&occur=13791
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