# Incorporating Context and External Knowledge for Pronoun Coreference Resolution

arXiv: https://arxiv.org/abs/1905.10238 · HTML: https://mlsanity.com/paper/1905.10238/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 May 2019.

## Built on by (most cited first, 8 of 8)

- [Improving Image Captioning via Predicting Structured Concepts](https://mlsanity.com/paper/2311.08223/) (2023)
- [VD-PCR: Improving Visual Dialog with Pronoun Coreference Resolution](https://mlsanity.com/paper/2205.14693/) (2022)
- [Joint Coreference Resolution and Character Linking for Multiparty Conversation](https://mlsanity.com/paper/2101.11204/) (2021)
- [A hybrid entity-centric approach to Persian pronoun resolution](https://mlsanity.com/paper/2211.06257/) (2022)
- [Representation Decomposition for Learning Similarity and Contrastness Across Modalities for Affective Computing](https://mlsanity.com/paper/2506.07086/) (2025)
- [Exophoric Pronoun Resolution in Dialogues with Topic Regularization](https://mlsanity.com/paper/2109.04787/) (2021)
- [Solving the Challenge Set without Solving the Task: On Winograd Schemas as a Test of Pronominal Coreference Resolution](https://mlsanity.com/paper/2410.09448/) (2024)
- [EMODIS: A Benchmark for Context-Dependent Emoji Disambiguation in Large Language Models](https://mlsanity.com/paper/2511.07193/) (2025)

## Abstract (arXiv)

Linking pronominal expressions to the correct references requires, in many cases, better analysis of the contextual information and external knowledge. In this paper, we propose a two-layer model for pronoun coreference resolution that leverages both context and external knowledge, where a knowledge attention mechanism is designed to ensure the model leveraging the appropriate source of external knowledge based on different context. Experimental results demonstrate the validity and effectiveness of our model, where it outperforms state-of-the-art models by a large margin.