Towards Evaluation of Cross-document Coreference Resolution Models Using Datasets with Diverse Annotation Schemes
Anastasia Zhukova, Felix Hamborg, and Bela Gipp

TL;DR
This paper compares two cross-document coreference resolution datasets with different annotation schemes, introduces a new metric for lexical diversity, and discusses implications for model evaluation and generalizability.
Contribution
It provides a detailed analysis of dataset annotation schemes, proposes a new phrasing diversity metric, and suggests a combined evaluation approach for better model generalization.
Findings
NewsWCL50 has more lexically diverse coreference chains than ECB+.
Annotating NewsWCL50 results in lower inter-coder reliability.
Different datasets create distinct tasks for CDCR models, such as lexical disambiguation.
Abstract
Established cross-document coreference resolution (CDCR) datasets contain event-centric coreference chains of events and entities with identity relations. These datasets establish strict definitions of the coreference relations across related tests but typically ignore anaphora with more vague context-dependent loose coreference relations. In this paper, we qualitatively and quantitatively compare the annotation schemes of ECB+, a CDCR dataset with identity coreference relations, and NewsWCL50, a CDCR dataset with a mix of loose context-dependent and strict coreference relations. We propose a phrasing diversity metric (PD) that encounters for the diversity of full phrases unlike the previously proposed metrics and allows to evaluate lexical diversity of the CDCR datasets in a higher precision. The analysis shows that coreference chains of NewsWCL50 are more lexically diverse than those…
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Taxonomy
TopicsNatural Language Processing Techniques · Topic Modeling · Biomedical Text Mining and Ontologies
