@inproceedings{hu-etal-2026-beyond-known,
title = "Beyond Known Event Types: Open-Domain Event Detection via Ontology-Guided Grounding{--}Discovery Resolution",
author = "Hu, Yuhan and
Qi, Yupeng and
Luo, Yongxi and
Su, Chen and
Huang, Shan and
Lyu, Ziyu",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.2001/",
pages = "42649--42667",
ISBN = "979-8-89176-462-0",
abstract = "Open-domain event detection aims to identify known event types while discovering previously unseen ones from unstructured documents. In practice, event detection systems often operate with an evolving event set rather than a complete type inventory. Under this setting, existing zero-shot event detection methods can fail in two ways when the event type is absent from the predefined set: the model may either miss the event or assign it to a plausible but wrong known type, hiding the need for discovery. To address this problem, we propose OGED, an $\underline{\textbf{O}}$ntology-$\underline{\textbf{G}}$uided open-domain $\underline{\textbf{E}}$vent $\underline{\textbf{D}}$etection framework for grounding{--}discovery resolution, i.e., deciding whether an event mention should be assigned to an existing type or should induce a new type. OGED organizes the available event types into a hierarchical search structure, uses cluster-level scheduler and adaptive planning to keep known-type assignment and new-type discovery jointly active, and applies retrieval-gated verification together with conflict selection to resolve competing assignments before output. Experiments on a unified benchmark with 564 event types show that OGED outperforms strong baselines on both known-type detection and unknown-type discovery. Code is available at https://github.com/chenzhouli/OGED."
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<abstract>Open-domain event detection aims to identify known event types while discovering previously unseen ones from unstructured documents. In practice, event detection systems often operate with an evolving event set rather than a complete type inventory. Under this setting, existing zero-shot event detection methods can fail in two ways when the event type is absent from the predefined set: the model may either miss the event or assign it to a plausible but wrong known type, hiding the need for discovery. To address this problem, we propose OGED, an \underlineOntology-\underlineGuided open-domain \underlineEvent \underlineDetection framework for grounding–discovery resolution, i.e., deciding whether an event mention should be assigned to an existing type or should induce a new type. OGED organizes the available event types into a hierarchical search structure, uses cluster-level scheduler and adaptive planning to keep known-type assignment and new-type discovery jointly active, and applies retrieval-gated verification together with conflict selection to resolve competing assignments before output. Experiments on a unified benchmark with 564 event types show that OGED outperforms strong baselines on both known-type detection and unknown-type discovery. Code is available at https://github.com/chenzhouli/OGED.</abstract>
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%0 Conference Proceedings
%T Beyond Known Event Types: Open-Domain Event Detection via Ontology-Guided Grounding–Discovery Resolution
%A Hu, Yuhan
%A Qi, Yupeng
%A Luo, Yongxi
%A Su, Chen
%A Huang, Shan
%A Lyu, Ziyu
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F hu-etal-2026-beyond-known
%X Open-domain event detection aims to identify known event types while discovering previously unseen ones from unstructured documents. In practice, event detection systems often operate with an evolving event set rather than a complete type inventory. Under this setting, existing zero-shot event detection methods can fail in two ways when the event type is absent from the predefined set: the model may either miss the event or assign it to a plausible but wrong known type, hiding the need for discovery. To address this problem, we propose OGED, an \underlineOntology-\underlineGuided open-domain \underlineEvent \underlineDetection framework for grounding–discovery resolution, i.e., deciding whether an event mention should be assigned to an existing type or should induce a new type. OGED organizes the available event types into a hierarchical search structure, uses cluster-level scheduler and adaptive planning to keep known-type assignment and new-type discovery jointly active, and applies retrieval-gated verification together with conflict selection to resolve competing assignments before output. Experiments on a unified benchmark with 564 event types show that OGED outperforms strong baselines on both known-type detection and unknown-type discovery. Code is available at https://github.com/chenzhouli/OGED.
%U https://aclanthology.org/2026.findings-emnlp.2001/
%P 42649-42667
Markdown (Informal)
[Beyond Known Event Types: Open-Domain Event Detection via Ontology-Guided Grounding–Discovery Resolution](https://aclanthology.org/2026.findings-emnlp.2001/) (Hu et al., Findings 2026)
ACL