7 Matching Annotations
  1. Oct 2018
  2. Aug 2017
    1. introduce topic modeling to those not yet fully converted aware of its potential.

      Is resistance futile?

    2. They’re powerful, widely applicable, easy to use, and difficult to understand — a dangerous combination.
    1. In writing the description of our reverse engineering work below, we deliberately avoid terms that are commonly used in Machine Learning, where labels are "true," "correct," or "gold standard." This linguistic distinction highlights the fundamentally different perspective that humanists have on classification as a tool. Our goal is not to create a system that mimics the decisions of a human annotator, but rather to better represent the porous boundaries between labels and identify the piles on which a story could have been placed over a century ago late on a cold wintry night in a dimly lit schoolhouse in eastern Jutland. We note the contrast between our use of computers to problematize existing distinctions and the common concern in the Humanities that computers deal only with binaries and black-and-white distinctions.

      Valuable insight and eloquently phrased.

    2. Our goal is not to treat existing classifications as "ground truth" labels and build machine learning tools to mimic them, but rather to use computation to better quantify the variability and uncertainty of those classifications.
    3. Our goal with this classification method can be seen as the inverse of usual machine learning classifiers.
  3. May 2014