# XCoref: Cross-document Coreference Resolution in the Wild

arXiv: https://arxiv.org/abs/2109.05252 · HTML: https://mlsanity.com/paper/2109.05252/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 49 references that are in the map.
- First posted on arXiv 11 Sep 2021.

## Builds on (most cited first, 12 of 12)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Advances in Pre-Training Distributed Word Representations](https://mlsanity.com/paper/1712.09405/) (2017)
- [Simple BERT Models for Relation Extraction and Semantic Role Labeling](https://mlsanity.com/paper/1904.05255/) (2019)
- [Improving Coreference Resolution by Learning Entity-Level Distributed Representations](https://mlsanity.com/paper/1606.01323/) (2016)
- [Revisiting Joint Modeling of Cross-document Entity and Event Coreference Resolution](https://mlsanity.com/paper/1906.01753/) (2019)
- [Cross-document Coreference Resolution over Predicted Mentions](https://mlsanity.com/paper/2106.01210/) (2021)
- [Resolving Event Coreference with Supervised Representation Learning and Clustering-Oriented Regularization](https://mlsanity.com/paper/1805.10985/) (2018)
- [Generalizing Cross-Document Event Coreference Resolution Across Multiple Corpora](https://mlsanity.com/paper/2011.12249/) (2020)
- [CDLM: Cross-Document Language Modeling](https://mlsanity.com/paper/2101.00406/) (2021)
- [Magnitude: A Fast, Efficient Universal Vector Embedding Utility Package](https://mlsanity.com/paper/1810.11190/) (2018)
- [Concept Identification of Directly and Indirectly Related Mentions Referring to Groups of Persons](https://mlsanity.com/paper/2107.00955/) (2021)

## Built on by (most cited first, 6 of 6)

- [What is Event Knowledge Graph: A Survey](https://mlsanity.com/paper/2112.15280/) (2021)
- [RISC: Generating Realistic Synthetic Bilingual Insurance Contract](https://mlsanity.com/paper/2304.04212/) (2023)
- [Link Prediction for Event Logs in the Process Industry](https://mlsanity.com/paper/2508.09096/) (2025)
- [Code Book for the Annotation of Diverse Cross-Document Coreference of Entities in News Articles](https://mlsanity.com/paper/2310.12064/) (2023)
- [Diverse Word Choices, Same Reference: Annotating Lexically-Rich Cross-Document Coreference](https://mlsanity.com/paper/2602.17424/) (2026)
- [Piecing Together Cross-Document Coreference Resolution Datasets: Systematic Dataset Analysis and Unification](https://mlsanity.com/paper/2603.00621/) (2026)

## Abstract (arXiv)

Datasets and methods for cross-document coreference resolution (CDCR) focus on events or entities with strict coreference relations. They lack, however, annotating and resolving coreference mentions with more abstract or loose relations that may occur when news articles report about controversial and polarized events. Bridging and loose coreference relations trigger associations that may lead to exposing news readers to bias by word choice and labeling. For example, coreferential mentions of "direct talks between U.S. President Donald Trump and Kim" such as "an extraordinary meeting following months of heated rhetoric" or "great chance to solve a world problem" form a more positive perception of this event. A step towards bringing awareness of bias by word choice and labeling is the reliable resolution of coreferences with high lexical diversity. We propose an unsupervised method named XCoref, which is a CDCR method that capably resolves not only previously prevalent entities, such as persons, e.g., "Donald Trump," but also abstractly defined concepts, such as groups of persons, "caravan of immigrants," events and actions, e.g., "marching to the U.S. border." In an extensive evaluation, we compare the proposed XCoref to a state-of-the-art CDCR method and a previous method TCA that resolves such complex coreference relations and find that XCoref outperforms these methods. Outperforming an established CDCR model shows that the new CDCR models need to be evaluated on semantically complex mentions with more loose coreference relations to indicate their applicability of models to resolve mentions in the "wild" of political news articles.