# Cross-document Coreference Resolution over Predicted Mentions

arXiv: https://arxiv.org/abs/2106.01210 · HTML: https://mlsanity.com/paper/2106.01210/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 27 references that are in the map.
- First posted on arXiv 2 Jun 2021.

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019)
- [End-to-end Neural Coreference Resolution](https://mlsanity.com/paper/1707.07045/) (2017)
- [BERT for Coreference Resolution: Baselines and Analysis](https://mlsanity.com/paper/1908.09091/) (2019)
- [Coreference Resolution as Query-based Span Prediction](https://mlsanity.com/paper/1911.01746/) (2019)
- [Revisiting Joint Modeling of Cross-document Entity and Event Coreference Resolution](https://mlsanity.com/paper/1906.01753/) (2019)
- [Resolving Event Coreference with Supervised Representation Learning and Clustering-Oriented Regularization](https://mlsanity.com/paper/1805.10985/) (2018)
- [Event Coreference Resolution by Iteratively Unfolding Inter-dependencies among Events](https://mlsanity.com/paper/1707.07344/) (2017)
- [CDLM: Cross-Document Language Modeling](https://mlsanity.com/paper/2101.00406/) (2021)
- [Paraphrasing vs Coreferring: Two Sides of the Same Coin](https://mlsanity.com/paper/2004.14979/) (2020)
- [A Hierarchical Distance-dependent Bayesian Model for Event Coreference Resolution](https://mlsanity.com/paper/1504.05929/) (2015)
- [Scalable Hierarchical Clustering with Tree Grafting](https://mlsanity.com/paper/2001.00076/) (2019)

## Built on by (most cited first, 29 of 29)

- [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://mlsanity.com/paper/2306.08302/) (2023)
- [When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunities](https://mlsanity.com/paper/2307.16376/) (2023)
- [LingMess: Linguistically Informed Multi Expert Scorers for Coreference Resolution](https://mlsanity.com/paper/2205.12644/) (2022)
- [AmbigDocs: Reasoning across Documents on Different Entities under the Same Name](https://mlsanity.com/paper/2404.12447/) (2024)
- [Realistic Evaluation Principles for Cross-document Coreference Resolution](https://mlsanity.com/paper/2106.04192/) (2021)
- [2 * n is better than n2: Decomposing Event Coreference Resolution into Two Tractable Problems](https://mlsanity.com/paper/2305.05672/) (2023)
- [What happens before and after: Multi-Event Commonsense in Event Coreference Resolution](https://mlsanity.com/paper/2302.09715/) (2023)
- [Contrastive Representation Learning for Cross-Document Coreference Resolution of Events and Entities](https://mlsanity.com/paper/2205.11438/) (2022)
- [Implicit meta-learning may lead language models to trust more reliable sources](https://mlsanity.com/paper/2310.15047/) (2023)
- [iFacetSum: Coreference-based Interactive Faceted Summarization for Multi-Document Exploration](https://mlsanity.com/paper/2109.11621/) (2021)
- [XCoref: Cross-document Coreference Resolution in the Wild](https://mlsanity.com/paper/2109.05252/) (2021)
- [Linear Cross-document Event Coreference Resolution with X-AMR](https://mlsanity.com/paper/2404.08656/) (2024)
- [Generating Harder Cross-document Event Coreference Resolution Datasets using Metaphoric Paraphrasing](https://mlsanity.com/paper/2407.11988/) (2024)
- [Any Other Thoughts, Hedgehog? Linking Deliberation Chains in Collaborative Dialogues](https://mlsanity.com/paper/2410.19301/) (2024)
- [Multimodal Cross-Document Event Coreference Resolution Using Linear Semantic Transfer and Mixed-Modality Ensembles](https://mlsanity.com/paper/2404.08949/) (2024)
- [Identifying and Investigating Global News Coverage of Critical Events Such as Disasters and Terrorist Attacks](https://mlsanity.com/paper/2506.12925/) (2025)
- [Synergetic Event Understanding: A Collaborative Approach to Cross-Document Event Coreference Resolution with Large Language Models](https://mlsanity.com/paper/2406.02148/) (2024)
- [Beyond Pairwise: Global Zero-shot Temporal Graph Generation](https://mlsanity.com/paper/2502.11114/) (2025)
- [Okay, Let's Do This! Modeling Event Coreference with Generated Rationales and Knowledge Distillation](https://mlsanity.com/paper/2404.03196/) (2024)
- [Inferring Scientific Cross-Document Coreference and Hierarchy with Definition-Augmented Relational Reasoning](https://mlsanity.com/paper/2409.15113/) (2024)
- [EventFull: Complete and Consistent Event Relation Annotation](https://mlsanity.com/paper/2412.12733/) (2024)
- [Large Language Models Are Effective Human Annotation Assistants, But Not Good Independent Annotators](https://mlsanity.com/paper/2503.06778/) (2025)
- [EasyECR: A Library for Easy Implementation and Evaluation of Event Coreference Resolution Models](https://mlsanity.com/paper/2406.14106/) (2024)
- [Link Prediction for Event Logs in the Process Industry](https://mlsanity.com/paper/2508.09096/) (2025)
- [Multi-Document Keyphrase Extraction: Dataset, Baselines and Review](https://mlsanity.com/paper/2110.01073/) (2021)
- [Filling in the Gaps: Efficient Event Coreference Resolution using Graph Autoencoder Networks](https://mlsanity.com/paper/2310.11965/) (2023)
- [Argument-Centric Causal Intervention Method for Mitigating Bias in Cross-Document Event Coreference Resolution](https://mlsanity.com/paper/2506.01488/) (2025)
- [Do Lexical and Contextual Coreference Resolution Systems Degrade Differently under Mention Noise? An Empirical Study on Scientific Software Mentions](https://mlsanity.com/paper/2604.02171/) (2026)
- [Follow the Entities: A Corpus Map for Agentic Search](https://mlsanity.com/paper/2609.37226/) (2026)

## Abstract (arXiv)

Coreference resolution has been mostly investigated within a single document scope, showing impressive progress in recent years based on end-to-end models. However, the more challenging task of cross-document (CD) coreference resolution remained relatively under-explored, with the few recent models applied only to gold mentions. Here, we introduce the first end-to-end model for CD coreference resolution from raw text, which extends the prominent model for within-document coreference to the CD setting. Our model achieves competitive results for event and entity coreference resolution on gold mentions. More importantly, we set first baseline results, on the standard ECB+ dataset, for CD coreference resolution over predicted mentions. Further, our model is simpler and more efficient than recent CD coreference resolution systems, while not using any external resources.