@inproceedings{cattan-etal-2023-champ,
title = "{CHAMP}: Efficient Annotation and Consolidation of Cluster Hierarchies",
author = "Cattan, Arie and
Hope, Tom and
Downey, Doug and
Bar-Haim, Roy and
Eden, Lilach and
Kantor, Yoav and
Dagan, Ido",
editor = "Feng, Yansong and
Lefever, Els",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-demo.37",
doi = "10.18653/v1/2023.emnlp-demo.37",
pages = "403--412",
abstract = "Various NLP tasks require a complex hierarchical structure over nodes, where each node is a cluster of items. Examples include generating entailment graphs, hierarchical cross-document coreference resolution, annotating event and subevent relations, etc. To enable efficient annotation of such hierarchical structures, we release CHAMP, an open source tool allowing to incrementally construct both clusters and hierarchy simultaneously over any type of texts. This incremental approach significantly reduces annotation time compared to the common pairwise annotation approach and also guarantees maintaining transitivity at the cluster and hierarchy levels. Furthermore, CHAMP includes a consolidation mode, where an adjudicator can easily compare multiple cluster hierarchy annotations and resolve disagreements.",
}
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<abstract>Various NLP tasks require a complex hierarchical structure over nodes, where each node is a cluster of items. Examples include generating entailment graphs, hierarchical cross-document coreference resolution, annotating event and subevent relations, etc. To enable efficient annotation of such hierarchical structures, we release CHAMP, an open source tool allowing to incrementally construct both clusters and hierarchy simultaneously over any type of texts. This incremental approach significantly reduces annotation time compared to the common pairwise annotation approach and also guarantees maintaining transitivity at the cluster and hierarchy levels. Furthermore, CHAMP includes a consolidation mode, where an adjudicator can easily compare multiple cluster hierarchy annotations and resolve disagreements.</abstract>
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%0 Conference Proceedings
%T CHAMP: Efficient Annotation and Consolidation of Cluster Hierarchies
%A Cattan, Arie
%A Hope, Tom
%A Downey, Doug
%A Bar-Haim, Roy
%A Eden, Lilach
%A Kantor, Yoav
%A Dagan, Ido
%Y Feng, Yansong
%Y Lefever, Els
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F cattan-etal-2023-champ
%X Various NLP tasks require a complex hierarchical structure over nodes, where each node is a cluster of items. Examples include generating entailment graphs, hierarchical cross-document coreference resolution, annotating event and subevent relations, etc. To enable efficient annotation of such hierarchical structures, we release CHAMP, an open source tool allowing to incrementally construct both clusters and hierarchy simultaneously over any type of texts. This incremental approach significantly reduces annotation time compared to the common pairwise annotation approach and also guarantees maintaining transitivity at the cluster and hierarchy levels. Furthermore, CHAMP includes a consolidation mode, where an adjudicator can easily compare multiple cluster hierarchy annotations and resolve disagreements.
%R 10.18653/v1/2023.emnlp-demo.37
%U https://aclanthology.org/2023.emnlp-demo.37
%U https://doi.org/10.18653/v1/2023.emnlp-demo.37
%P 403-412
Markdown (Informal)
[CHAMP: Efficient Annotation and Consolidation of Cluster Hierarchies](https://aclanthology.org/2023.emnlp-demo.37) (Cattan et al., EMNLP 2023)
ACL
- Arie Cattan, Tom Hope, Doug Downey, Roy Bar-Haim, Lilach Eden, Yoav Kantor, and Ido Dagan. 2023. CHAMP: Efficient Annotation and Consolidation of Cluster Hierarchies. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 403–412, Singapore. Association for Computational Linguistics.