Automated classification for open-ended questions + Hammock Plots

Answers to open-ended questions are often manually coded into different categories. This is time consuming. Automated coding uses statistical/machine learning to train on a small subset of manually coded text answers. The state of the art in NLP (natural language processing) has shifted: A general language model is first pre-trained on vast amounts of unrelated data, and then this model is adapted to a specific application data set. After reviewing some earlier results, we empirically investigate whether BERT, the currently dominant pre-trained language model, is more effective at automated coding of answers to open-ended questions than non-pre-trained statistical learning approaches. In the second part of the talk, I discuss the hammock plot for visualizing categorical or mixed categorical data.

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