Seeded Hierarchical Clustering for Expert-Crafted Taxonomies

• URL: http://arxiv.org/abs/2205.11602v1
• Date: Mon, 23 May 2022 19:58:06 GMT
• Title: Seeded Hierarchical Clustering for Expert-Crafted Taxonomies
• Authors: Anish Saha, Amith Ananthram, Emily Allaway, Heng Ji, Kathleen McKeown
• Abstract summary: We propose HierSeed, a weakly supervised algorithm for fitting unlabeled corpora. It is both data and efficient. It outperforms both unsupervised and supervised baselines for the SHC task on three real-world datasets.
• Score: 48.10324642720299
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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