Seeded hierarchical clustering for expert-crafted taxonomies

Anish Saha; Amith  Ananthram; Emily Allaway; Heng Ji; Kathleen McKeown

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

Seeded hierarchical clustering for expert-crafted taxonomies

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract / Description of output

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.

Original language	English
Title of host publication	Findings of the Association for Computational Linguistics: EMNLP 2022
Editors	Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Place of Publication	Abu Dhabi, United Arab Emirates
Publisher	Association for Computational Linguistics
Pages	1595-1609
Number of pages	15
Edition	3
ISBN (Electronic)	9781959429432
DOIs	https://doi.org/10.18653/v1/2022.findings-emnlp.115
Publication status	Published - 11 Dec 2022
Event	The 2022 Conference on Empirical Methods in Natural Language Processing - Abu Dhabi National Exhibition Centre, Abu Dhabi, United Arab Emirates Duration: 7 Dec 2022 → 11 Dec 2022 Conference number: 27 https://2022.emnlp.org/

Publication series

Name	Findings of the Association for Computational Linguistics
Publisher	ACL
ISSN (Print)	0891-2017
ISSN (Electronic)	1530-9312

Conference

Conference	The 2022 Conference on Empirical Methods in Natural Language Processing
Abbreviated title	EMNLP 2022
Country/Territory	United Arab Emirates
City	Abu Dhabi
Period	7/12/22 → 11/12/22
Internet address	https://2022.emnlp.org/

Access to Document

10.18653/v1/2022.findings-emnlp.115Licence: Creative Commons: Attribution (CC-BY)

SahaEtalEMNLP2022SeededHierarchicalClusteringFinal published version, 3.08 MBLicence: Creative Commons: Attribution (CC-BY)

Cite this

Saha, A., Ananthram, A., Allaway, E., Ji, H., & McKeown, K. (2022). Seeded hierarchical clustering for expert-crafted taxonomies. In Y. Goldberg, Z. Kozareva, & Y. Zhang (Eds.), Findings of the Association for Computational Linguistics: EMNLP 2022 (3 ed., pp. 1595-1609). (Findings of the Association for Computational Linguistics). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.findings-emnlp.115

Saha, Anish ; Ananthram, Amith ; Allaway, Emily et al. / Seeded hierarchical clustering for expert-crafted taxonomies. Findings of the Association for Computational Linguistics: EMNLP 2022. editor / Yoav Goldberg ; Zornitsa Kozareva ; Yue Zhang. 3. ed. Abu Dhabi, United Arab Emirates : Association for Computational Linguistics, 2022. pp. 1595-1609 (Findings of the Association for Computational Linguistics).

@inproceedings{70368e4a3f4d4284ac672d9a18594c60,

title = "Seeded hierarchical clustering for expert-crafted taxonomies",

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.",

author = "Anish Saha and Amith Ananthram and Emily Allaway and Heng Ji and Kathleen McKeown",

year = "2022",

month = dec,

day = "11",

doi = "10.18653/v1/2022.findings-emnlp.115",

language = "English",

series = "Findings of the Association for Computational Linguistics",

publisher = "Association for Computational Linguistics",

pages = "1595--1609",

editor = "Yoav Goldberg and Zornitsa Kozareva and Yue Zhang",

booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",

edition = "3",

note = "The 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022 ; Conference date: 07-12-2022 Through 11-12-2022",

url = "https://2022.emnlp.org/",

}

Saha, A, Ananthram, A, Allaway, E, Ji, H & McKeown, K 2022, Seeded hierarchical clustering for expert-crafted taxonomies. in Y Goldberg, Z Kozareva & Y Zhang (eds), Findings of the Association for Computational Linguistics: EMNLP 2022. 3 edn, Findings of the Association for Computational Linguistics, Association for Computational Linguistics, Abu Dhabi, United Arab Emirates, pp. 1595-1609, The 2022 Conference on Empirical Methods in Natural Language Processing, Abu Dhabi, United Arab Emirates, 7/12/22. https://doi.org/10.18653/v1/2022.findings-emnlp.115

Seeded hierarchical clustering for expert-crafted taxonomies. / Saha, Anish; Ananthram, Amith; Allaway, Emily et al.
Findings of the Association for Computational Linguistics: EMNLP 2022. ed. / Yoav Goldberg; Zornitsa Kozareva; Yue Zhang. 3. ed. Abu Dhabi, United Arab Emirates: Association for Computational Linguistics, 2022. p. 1595-1609 (Findings of the Association for Computational Linguistics).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Seeded hierarchical clustering for expert-crafted taxonomies

AU - Saha, Anish

AU - Ananthram, Amith

AU - Allaway, Emily

AU - Ji, Heng

AU - McKeown, Kathleen

N1 - Conference code: 27

PY - 2022/12/11

Y1 - 2022/12/11

N2 - 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.

AB - 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.

U2 - 10.18653/v1/2022.findings-emnlp.115

DO - 10.18653/v1/2022.findings-emnlp.115

M3 - Conference contribution

T3 - Findings of the Association for Computational Linguistics

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EP - 1609

BT - Findings of the Association for Computational Linguistics: EMNLP 2022

A2 - Goldberg, Yoav

A2 - Kozareva, Zornitsa

A2 - Zhang, Yue

PB - Association for Computational Linguistics

CY - Abu Dhabi, United Arab Emirates

T2 - The 2022 Conference on Empirical Methods in Natural Language Processing

Y2 - 7 December 2022 through 11 December 2022

ER -

Saha A, Ananthram A, Allaway E, Ji H, McKeown K. Seeded hierarchical clustering for expert-crafted taxonomies. In Goldberg Y, Kozareva Z, Zhang Y, editors, Findings of the Association for Computational Linguistics: EMNLP 2022. 3 ed. Abu Dhabi, United Arab Emirates: Association for Computational Linguistics. 2022. p. 1595-1609. (Findings of the Association for Computational Linguistics). doi: 10.18653/v1/2022.findings-emnlp.115

Seeded hierarchical clustering for expert-crafted taxonomies

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Publication series

Conference

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