SAP-sLDA: An Interpretable Interface for Exploring Unstructured Text
This work addresses the challenge of creating interpretable text projections for users exploring unstructured text corpora, though it appears incremental as it builds on existing LDA methods.
The authors tackled the problem of low-dimensional text projections not aligning with human notions of document similarity by proposing a semi-supervised human-in-the-loop LDA-based method, which yields more interpretable projections with only a fraction of labels provided on synthetic corpora and similar qualitative results on real data.
A common way to explore text corpora is through low-dimensional projections of the documents, where one hopes that thematically similar documents will be clustered together in the projected space. However, popular algorithms for dimensionality reduction of text corpora, like Latent Dirichlet Allocation (LDA), often produce projections that do not capture human notions of document similarity. We propose a semi-supervised human-in-the-loop LDA-based method for learning topics that preserve semantically meaningful relationships between documents in low-dimensional projections. On synthetic corpora, our method yields more interpretable projections than baseline methods with only a fraction of labels provided. On a real corpus, we obtain qualitatively similar results.