Hierarchically Supervised Latent Dirichlet Allocation

We introduce hierarchically supervised latent Dirichlet allocation (HSLDA), a model for hierarchically and multiply labeled bag-of-word data. Examples of such data include web pages and their placement in directories, product descriptions and associated categories from product hierarchies, and free-text clinical records and their assigned diagnosis codes. Out-of-sample label prediction is the primary goal of this work, but improved lower-dimensional representations of the bag-of-word data are also of interest. We demonstrate HSLDA on large-scale data from clinical document labeling and retail product categorization tasks. We show that leveraging the structure from hierarchical labels improves out-of-sample label prediction substantially when compared to models that do not.

Authors

• Adler Perotte
• Frank Wood University of British Columbia Amii InvertedAI
• Noemie Elhadad
• Nicholas Bartlett

Keywords

No keywords are indexed for this paper.