  The most common ways to explore latent document dimensions are topic models
and clustering methods. However, topic models have several drawbacks: e.g.,
they require us to choose the number of latent dimensions a priori, and the
results are stochastic. Most clustering methods have the same issues and lack
flexibility in various ways, such as not accounting for the influence of
different topics on single documents, forcing word-descriptors to belong to a
single topic (hard-clustering) or necessarily relying on word representations.
We propose PROgressive SImilarity Thresholds - ProSiT, a deterministic and
interpretable method, agnostic to the input format, that finds the optimal
number of latent dimensions and only has two hyper-parameters, which can be set
efficiently via grid search. We compare this method with a wide range of topic
models and clustering methods on four benchmark data sets. In most setting,
ProSiT matches or outperforms the other methods in terms six metrics of topic
coherence and distinctiveness, producing replicable, deterministic results.
