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SAP-sLDA: An Interpretable Interface for Exploring Unstructured Text
Charumathi Badrinath · Weiwei Pan · Finale Doshi-Velez
Event URL: https://openreview.net/forum?id=WAUBMB3ZKv »

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 SAP-sLDA, a semi-supervised human-in-the-loop method for learning topics that preserve semantically meaningful relationships between documents in low-dimensional projections. On synthetic corpora, SAP-sLDA yields more interpretable projections than baseline methods with only a fraction of labels provided. On a real corpus, we obtain qualitatively similar results.

Author Information

Charumathi Badrinath (Harvard University)
Weiwei Pan (Harvard University)
Finale Doshi-Velez (Harvard University)
Finale Doshi-Velez

Finale Doshi-Velez is a Gordon McKay Professor in Computer Science at the Harvard Paulson School of Engineering and Applied Sciences. She completed her MSc from the University of Cambridge as a Marshall Scholar, her PhD from MIT, and her postdoc at Harvard Medical School. Her interests lie at the intersection of machine learning, healthcare, and interpretability. Selected Additional Shinies: BECA recipient, AFOSR YIP and NSF CAREER recipient; Sloan Fellow; IEEE AI Top 10 to Watch

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