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.
| Original language | English |
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| Pages | 403-412 |
| Number of pages | 10 |
| State | Published - 2023 |
| Event | 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023 - Hybrid, Singapore, Singapore Duration: 6 Dec 2023 → 10 Dec 2023 |
Conference
| Conference | 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023 |
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| Country/Territory | Singapore |
| City | Hybrid, Singapore |
| Period | 6/12/23 → 10/12/23 |
Bibliographical note
Publisher Copyright:© 2023 Association for Computational Linguistics.
Funding
This work was supported by the Israel Science Foundation (grant no. 2827/21). Arie Cattan is partially supported by the PBC fellowship for outstanding PhD candidates in data science.
| Funders | Funder number |
|---|---|
| Israel Science Foundation | 2827/21 |
| Planning and Budgeting Committee of the Council for Higher Education of Israel |