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        <title>Seeded Hierarchical Clustering for Expert-Crafted Taxonomies</title>
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    <name type="personal">
        <namePart type="given">Anish</namePart>
        <namePart type="family">Saha</namePart>
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        <namePart type="given">Amith</namePart>
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Heng</namePart>
        <namePart type="family">Ji</namePart>
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    <name type="personal">
        <namePart type="given">Kathleen</namePart>
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            <namePart type="given">Yue</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/2022.findings-emnlp.115</identifier>
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%0 Conference Proceedings
%T Seeded Hierarchical Clustering for Expert-Crafted Taxonomies
%A Saha, Anish
%A Ananthram, Amith
%A Allaway, Emily
%A Ji, Heng
%A McKeown, Kathleen
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F saha-etal-2022-seeded
%X 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.
%R 10.18653/v1/2022.findings-emnlp.115
%U https://aclanthology.org/2022.findings-emnlp.115
%U https://doi.org/10.18653/v1/2022.findings-emnlp.115
%P 1595-1609

Markdown (Informal)

[Seeded Hierarchical Clustering for Expert-Crafted Taxonomies](https://aclanthology.org/2022.findings-emnlp.115) (Saha et al., Findings 2022)

• Seeded Hierarchical Clustering for Expert-Crafted Taxonomies (Saha et al., Findings 2022)

ACL

• Anish Saha, Amith Ananthram, Emily Allaway, Heng Ji, and Kathleen McKeown. 2022. Seeded Hierarchical Clustering for Expert-Crafted Taxonomies. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 1595–1609, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.