CLUE: A Contrastive Learning Framework for Unsupervised Event Type Induction and Annotation

Pratik Maitra; Lauren Kwon; Qi Li

CLUE: A Contrastive Learning Framework for Unsupervised Event Type Induction and Annotation

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Abstract

Event ontology is the foundational framework for defining events in a dataset. Event type induction generally involves domain experts who define guidelines for creating event types and their names based on the dataset’s domain and scope. However, this process is resource-intensive in terms of annotation costs and is prone to human biases. To alleviate the need for manual event ontology creation, we propose the CLUE framework for event type induction, naming, and annotation. Our main contributions in this work are: 1) designing a novel joint embedding representation for expressing both the verb-centric view and the semantic view of events; 2) event type induction using contrastive loss guided clustering; 3) event naming and refinement. Experiments demonstrate the effectiveness of our method.

Anthology ID:: 2026.findings-aacl.106
Volume:: Findings of the Association for Computational Linguistics: IJCNLP-AACL 2026
Month:: November
Year:: 2026
Address:: Hengqin, China
Editors:: Yue Zhang, Ruifeng Xu, Ayu Purwarianti, Tong Rong, Jun Suzuki
Venue:: Findings
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 2094–2111
Language:
URL:: https://aclanthology.org/2026.findings-aacl.106/
DOI:
Bibkey:
Cite (ACL):: Pratik Maitra, Lauren Kwon, and Qi Li. 2026. CLUE: A Contrastive Learning Framework for Unsupervised Event Type Induction and Annotation. In Findings of the Association for Computational Linguistics: IJCNLP-AACL 2026, pages 2094–2111, Hengqin, China. Association for Computational Linguistics.
Cite (Informal):: CLUE: A Contrastive Learning Framework for Unsupervised Event Type Induction and Annotation (Maitra et al., Findings 2026)
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PDF:: https://aclanthology.org/2026.findings-aacl.106.pdf

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@inproceedings{maitra-etal-2026-clue,
    title = "{CLUE}: A Contrastive Learning Framework for Unsupervised Event Type Induction and Annotation",
    author = "Maitra, Pratik  and
      Kwon, Lauren  and
      Li, Qi",
    editor = "Zhang, Yue  and
      Xu, Ruifeng  and
      Purwarianti, Ayu  and
      Rong, Tong  and
      Suzuki, Jun",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {IJCNLP}-{AACL} 2026",
    month = nov,
    year = "2026",
    address = "Hengqin, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.findings-aacl.106/",
    pages = "2094--2111",
    ISBN = "979-8-89176-444-6",
    abstract = "Event ontology is the foundational framework for defining events in a dataset. Event type induction generally involves domain experts who define guidelines for creating event types and their names based on the dataset{'}s domain and scope. However, this process is resource-intensive in terms of annotation costs and is prone to human biases. To alleviate the need for manual event ontology creation, we propose the CLUE framework for event type induction, naming, and annotation. Our main contributions in this work are: 1) designing a novel joint embedding representation for expressing both the verb-centric view and the semantic view of events; 2) event type induction using contrastive loss guided clustering; 3) event naming and refinement. Experiments demonstrate the effectiveness of our method."
}

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%0 Conference Proceedings
%T CLUE: A Contrastive Learning Framework for Unsupervised Event Type Induction and Annotation
%A Maitra, Pratik
%A Kwon, Lauren
%A Li, Qi
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Findings of the Association for Computational Linguistics: IJCNLP-AACL 2026
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-444-6
%F maitra-etal-2026-clue
%X Event ontology is the foundational framework for defining events in a dataset. Event type induction generally involves domain experts who define guidelines for creating event types and their names based on the dataset’s domain and scope. However, this process is resource-intensive in terms of annotation costs and is prone to human biases. To alleviate the need for manual event ontology creation, we propose the CLUE framework for event type induction, naming, and annotation. Our main contributions in this work are: 1) designing a novel joint embedding representation for expressing both the verb-centric view and the semantic view of events; 2) event type induction using contrastive loss guided clustering; 3) event naming and refinement. Experiments demonstrate the effectiveness of our method.
%U https://aclanthology.org/2026.findings-aacl.106/
%P 2094-2111

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Markdown (Informal)

[CLUE: A Contrastive Learning Framework for Unsupervised Event Type Induction and Annotation](https://aclanthology.org/2026.findings-aacl.106/) (Maitra et al., Findings 2026)

CLUE: A Contrastive Learning Framework for Unsupervised Event Type Induction and Annotation (Maitra et al., Findings 2026)

ACL

Pratik Maitra, Lauren Kwon, and Qi Li. 2026. CLUE: A Contrastive Learning Framework for Unsupervised Event Type Induction and Annotation. In Findings of the Association for Computational Linguistics: IJCNLP-AACL 2026, pages 2094–2111, Hengqin, China. Association for Computational Linguistics.