Comparing Apples to Apple: The Effects of Stemmers on Topic Models
Abstract
Rule-based stemmers such as the Porter stemmer are frequently used to preprocess English corpora for topic modeling. In this work, we train and evaluate topic models on a variety of corpora using several different stemming algorithms. We examine several different quantitative measures of the resulting models, including likelihood, coherence, model stability, and entropy. Despite their frequent use in topic modeling, we find that stemmers produce no meaningful improvement in likelihood and coherence and in fact can degrade topic stability.Author Biography
Alexandra Schofield
PhD Student, Department of Computer Science
David Mimno
Assistant Professor, Department of Information Science