Low-dimensional Embeddings for Interpretable Anchor-based Topic Inference
David Mimno, Moontae Lee · 2014
The anchor words algorithm performs provably efficient topic model inference by finding an approximate convex hull in a high-dimensional word co-occurrence space.However, the existing greedy algorithm often selects poor anchor words, reducing topic quality and interpretability.Rather than finding an approximate convex hull in a high-dimensional space, we propose to find an exact convex hull in a visualizable 2-or 3-dimensional space.Such low-dimensional embeddings both improve topics and clearly show users why the algorithm selects certain words.