  Topic models are a useful analysis tool to uncover the underlying themes
within document collections. The dominant approach is to use probabilistic
topic models that posit a generative story, but in this paper we propose an
alternative way to obtain topics: clustering pre-trained word embeddings while
incorporating document information for weighted clustering and reranking top
words. We provide benchmarks for the combination of different word embeddings
and clustering algorithms, and analyse their performance under dimensionality
reduction with PCA. The best performing combination for our approach performs
as well as classical topic models, but with lower runtime and computational
complexity.
