Coreference resolution is typically evaluated using aggregate statistical metrics such as CoNLL-F1, which measure structural overlap between predicted and gold clusters. While widely used, these metrics offer limited diagnostic insights, penalizing errors without revealing whether a system struggles with specific semantic categories, such as people, locations, or events, and making it difficult to interpret model capabilities or derive actionable improvements. We address this gap by introducing a semantically-enhanced evaluation framework for coreference resolution. Our approach overlays Concept and Named Entity Recognition (CNER) onto coreference outputs, assigning semantic labels to nominal mentions and propagating them to entire coreference clusters. This enables the computation of typed scores aimed at evaluating mention extraction and linking capabilities stratified by semantic class. Across our experiments on OntoNotes, LitBank, and PreCo, we show that our framework uncovers systematic weaknesses that remain obscured by aggregate metrics. Furthermore, we show that these diagnostics can be used to design targeted, low-cost data augmentation strategies, achieving measurable out-of-domain improvements.

Current coreference resolution systems are typically tailored for short- or medium-sized texts and struggle to scale to very long documents due to architectural limitations and implied memory costs.However, a few available solutions can be applied by inputting documents split into smaller windows. This is inherently similar to what happens in the cross-document setting, in which systems infer coreference relations between mentions that are found in separate documents.In this paper, we unify these two challenging settings under the general framework of cross-context coreference, and introduce xCoRe, a new unified approach designed to efficiently handle short-, long-, and cross-document coreference resolution.xCoRe adopts a three-step pipeline that first identifies mentions, then creates clusters within individual contexts, and finally merges clusters across contexts.In our experiments, we show that our formulation enables joint training on shared long- and cross-document resources, increasing data availability and particularly benefiting the challenging cross-document task.Our model achieves new state-of-the-art results on cross-document benchmarks and strong performance on long-document data, while retaining top-tier results on traditional datasets, positioning it as a robust, versatile solution that can be applied across all end-to-end coreference settings.We release our models and code at http://github.com/sapienzanlp/xcore.