# A Hierarchical Distance-dependent Bayesian Model for Event Coreference Resolution

arXiv: https://arxiv.org/abs/1504.05929 · HTML: https://mlsanity.com/paper/1504.05929/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Apr 2015.

## Built on by (most cited first, 12 of 12)

- [Cross-document Coreference Resolution over Predicted Mentions](https://mlsanity.com/paper/2106.01210/) (2021)
- [AmbigDocs: Reasoning across Documents on Different Entities under the Same Name](https://mlsanity.com/paper/2404.12447/) (2024)
- [Focus on what matters: Applying Discourse Coherence Theory to Cross Document Coreference](https://mlsanity.com/paper/2110.05362/) (2021)
- [Realistic Evaluation Principles for Cross-document Coreference Resolution](https://mlsanity.com/paper/2106.04192/) (2021)
- [Sequential Cross-Document Coreference Resolution](https://mlsanity.com/paper/2104.08413/) (2021)
- [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)
- [Synergetic Event Understanding: A Collaborative Approach to Cross-Document Event Coreference Resolution with Large Language Models](https://mlsanity.com/paper/2406.02148/) (2024)
- [CorefPrompt: Prompt-based Event Coreference Resolution by Measuring Event Type and Argument Compatibilities](https://mlsanity.com/paper/2310.14512/) (2023)
- [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)
- [EasyECR: A Library for Easy Implementation and Evaluation of Event Coreference Resolution Models](https://mlsanity.com/paper/2406.14106/) (2024)

## Abstract (arXiv)

We present a novel hierarchical distance-dependent Bayesian model for event coreference resolution. While existing generative models for event coreference resolution are completely unsupervised, our model allows for the incorporation of pairwise distances between event mentions -- information that is widely used in supervised coreference models to guide the generative clustering processing for better event clustering both within and across documents. We model the distances between event mentions using a feature-rich learnable distance function and encode them as Bayesian priors for nonparametric clustering. Experiments on the ECB+ corpus show that our model outperforms state-of-the-art methods for both within- and cross-document event coreference resolution.