# Learning Global Features for Coreference Resolution

arXiv: https://arxiv.org/abs/1604.03035 · HTML: https://mlsanity.com/paper/1604.03035/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2016.

## Built on by (most cited first, 20 of 20)

- [VisoGender: A dataset for benchmarking gender bias in image-text pronoun resolution](https://mlsanity.com/paper/2306.12424/) (2023)
- [OntoGUM: Evaluating Contextualized SOTA Coreference Resolution on 12 More Genres](https://mlsanity.com/paper/2106.00933/) (2021)
- [Sequential Cross-Document Coreference Resolution](https://mlsanity.com/paper/2104.08413/) (2021)
- [End-to-end Neural Coreference Resolution Revisited: A Simple yet Effective Baseline](https://mlsanity.com/paper/2107.01700/) (2021)
- [Graph Refinement for Coreference Resolution](https://mlsanity.com/paper/2203.16574/) (2022)
- [Who are you referring to? Coreference resolution in image narrations](https://mlsanity.com/paper/2211.14563/) (2022)
- [Transformers as Graph-to-Graph Models](https://mlsanity.com/paper/2310.17936/) (2023)
- [Scoring Coreference Chains with Split-Antecedent Anaphors](https://mlsanity.com/paper/2205.12323/) (2022)
- [Longtonotes: OntoNotes with Longer Coreference Chains](https://mlsanity.com/paper/2210.03650/) (2022)
- [A Survey on Semantic Processing Techniques](https://mlsanity.com/paper/2310.18345/) (2023)
- [Stay Together: A System for Single and Split-antecedent Anaphora Resolution](https://mlsanity.com/paper/2104.05320/) (2021)
- [Anatomy of OntoGUM--Adapting GUM to the OntoNotes Scheme to Evaluate Robustness of SOTA Coreference Algorithms](https://mlsanity.com/paper/2110.05727/) (2021)
- [RGAT: A Deeper Look into Syntactic Dependency Information for Coreference Resolution](https://mlsanity.com/paper/2309.04977/) (2023)
- [Hybrid Rule-Neural Coreference Resolution System based on Actor-Critic Learning](https://mlsanity.com/paper/2212.10087/) (2022)
- [Coreference Resolution: Are the eliminated spans totally worthless?](https://mlsanity.com/paper/2101.00737/) (2021)
- [Review of coreference resolution in English and Persian](https://mlsanity.com/paper/2211.04428/) (2022)
- [SPLICE: A Singleton-Enhanced PipeLIne for Coreference REsolution](https://mlsanity.com/paper/2403.17245/) (2024)
- [Efficient Sample-Specific Encoder Perturbations](https://mlsanity.com/paper/2405.01601/) (2024)
- [Persian Pronoun Resolution: Leveraging Neural Networks and Language Models](https://mlsanity.com/paper/2405.10714/) (2024)
- [CorefInst: Leveraging LLMs for Multilingual Coreference Resolution](https://mlsanity.com/paper/2509.17505/) (2025)

## Abstract (arXiv)

There is compelling evidence that coreference prediction would benefit from modeling global information about entity-clusters. Yet, state-of-the-art performance can be achieved with systems treating each mention prediction independently, which we attribute to the inherent difficulty of crafting informative cluster-level features. We instead propose to use recurrent neural networks (RNNs) to learn latent, global representations of entity clusters directly from their mentions. We show that such representations are especially useful for the prediction of pronominal mentions, and can be incorporated into an end-to-end coreference system that outperforms the state of the art without requiring any additional search.