  Topic models are useful tools for discovering latent semantic structures in
large textual corpora. Topic modeling historically relied on bag-of-words
representations of language. This approach makes models sensitive to the
presence of stop words and noise, and does not utilize potentially useful
contextual information. Recent efforts have been oriented at incorporating
contextual neural representations in topic modeling and have been shown to
outperform classical topic models. These approaches are, however, typically
slow, volatile and still require preprocessing for optimal results. We present
Semantic Signal Separation ($S^3$), a theory-driven topic modeling approach in
neural embedding spaces. $S^3$ conceptualizes topics as independent axes of
semantic space, and uncovers these with blind-source separation. Our approach
provides the most diverse, highly coherent topics, requires no preprocessing,
and is demonstrated to be the fastest contextually sensitive topic model to
date. We offer an implementation of $S^3$, among other approaches, in the
Turftopic Python package.
