EMNLP 2022finding1 citations
Seeded Hierarchical Clustering for Expert-Crafted Taxonomies
Anish Saha, Amith Ananthram, Emily Allaway, Heng Ji, Kathleen McKeown
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 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 in a computation and data efficient manner. HierSeed assigns documents to topics by weighing document density against topic hierarchical structure. It outperforms unsupervised and supervised baselines for the SHC task on three real-world datasets.
BibTeX
@inproceedings{saha-etal-2022-seeded,
title = "Seeded Hierarchical Clustering for Expert-Crafted Taxonomies",
author = "Saha, Anish and
Ananthram, Amith and
Allaway, Emily and
Ji, Heng and
McKeown, Kathleen",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.115/",
doi = "10.18653/v1/2022.findings-emnlp.115",
pages = "1595--1609"
}