  Practitioners from many disciplines (e.g., political science) use
expert-crafted taxonomies to make sense of large, unlabeled corpora. In this
work, we study Seeded Hierarchical Clustering (SHC): the task of automatically
fitting unlabeled data to such taxonomies using only a small set of labeled
examples. We propose HierSeed, a novel weakly supervised algorithm for this
task that uses only a small set of labeled seed examples. It is both data and
computationally efficient. HierSeed assigns documents to topics by weighing
document density against topic hierarchical structure. It outperforms both
unsupervised and supervised baselines for the SHC task on three real-world
datasets.
