My final project for Stanford CS 224U was on automatically assessing integrative complexity. I drew on work I’d previously done that demonstrated ongoing value from this political psychology construct, but I had not previously tried to automatically code for this construct. The code is available on github.
Integrative complexity is a construct from political psychology that measures semantic complexity in discourse. Although this metric has been shown useful in predicting violence and understanding elections, it is very time-consuming for analysts to assess. We describe a theory-driven automated system that improves the state-of-the-art for this task from Pearson’s r = 0.57 to r = 0.73 through framing the task as ordinal regression, leveraging dense vector representations of words, and developing syntactic and semantic features that go beyond lexical phrase matching. Our approach is less labor-intensive and more transferable than the previous state-of-the-art for this task.