Work

Seeded Hierarchical Clustering for Expert-Crafted Taxonomies

Year: 2022

Type: conference-paper

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 HI-ERSEED, 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.

Source: Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP)

DOI: 10.18653/v1/2022.findings-emnlp.115

Authors: Anish Saha, Amith Ananthram, Emily Allaway, Heng Ji, Kathleen McKeown

Institutions: Columbia University, University of Illinois Urbana-Champaign

Language: English

Topic: Advanced Clustering Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Science Foundation, Defense Advanced Research Projects Agency

Awards: G1163247334, G4713059963, G4818418728, Graduate Research Fellowship Program (GRFP)