CHAMP: Efficient Annotation and Consolidation of Cluster Hierarchies
Arie Cattan Tom Hope Doug Downey Roy Bar-Haim Lilach Eden Yoav Kantor Ido Dagan
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.