NYU Stern
Title: Scaling Latent Quantities from Text: From Black-and-White to Shades of Gray
Abstract: Probabilistic methods for classifying texts according to the likelihood of class membership form a rich tradition in machine learning and natural language processing. For many important problems, however, class prediction is either uninteresting, because it is known, or uninformative, because it yields poor information about a latent quantity of interest. In scaling political speeches, for instance, party membership is both known and uninformative, in the sense that in systems with party discipline, what is interesting is a latent trait in the speech, such as ideological position, often at odds with party membership. Predictive tools common in machine learning, where the goal is to predict a black-or-white class–such as spam, sentiment, or authorship–are not directly designed for the measurement problem of estimating latent quantities, especially those that are not inherently unobservable through direct means. In this talk, I present a method for modeling texts not as black or white representations, but rather as explicit mixtures of perspectives. The focus shifts from predicting an unobserved discrete label to estimating the mixture proportions expressed in a text. In this “shades of gray” worldview, we are able to estimate not only the graynesses of texts but also those of the words making up a text, using likelihood-based inference. While this method is novel in its application to text, it be can situated in and compared to known approaches such as dictionary methods, topic models, and the wordscores scaling method. This new method has a fundamental linguistic and statistical foundation, and exploring this foundation exposes implicit assumptions found in previous approaches. I explore the robustness properties of the method and discuss issues of uncertainty quantification. My motivating application throughout the talk will be scaling legislative debate speeches. (Joint work with Ken Benoit, London School of Economics.)