Tired of Topic Models? Clusters of Pretrained Word Embeddings Make for Fast and Good Topics too!
This work addresses the need for faster and simpler topic modeling methods for researchers and practitioners analyzing document collections, though it is incremental as it builds on existing embedding and clustering techniques.
The authors tackled the problem of topic modeling by proposing an alternative approach that clusters pre-trained word embeddings with document information, achieving performance comparable to classical topic models while reducing runtime and computational complexity.
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.