# Enhancing Coreference Resolution with Pretrained Language Models: Bridging the Gap Between Syntax and Semantics

arXiv: https://arxiv.org/abs/2504.05855 · HTML: https://mlsanity.com/paper/2504.05855/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 32 references that are in the map.
- First posted on arXiv 8 Apr 2025.

## Builds on (most cited first, 16 of 16)

- [Seeing Things from a Different Angle: Discovering Diverse Perspectives about Claims](https://mlsanity.com/paper/1906.03538/) (2019)
- [RA-ISF: Learning to Answer and Understand from Retrieval Augmentation via Iterative Self-Feedback](https://mlsanity.com/paper/2403.06840/) (2024)
- [Maverick: Efficient and Accurate Coreference Resolution Defying Recent Trends](https://mlsanity.com/paper/2407.21489/) (2024)
- [Bridging Context Gaps: Leveraging Coreference Resolution for Long Contextual Understanding](https://mlsanity.com/paper/2410.01671/) (2024)
- [SciCo: Hierarchical Cross-Document Coreference for Scientific Concepts](https://mlsanity.com/paper/2104.08809/) (2021)
- [WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets](https://mlsanity.com/paper/2010.08232/) (2020)
- [Contrastive Representation Learning for Cross-Document Coreference Resolution of Events and Entities](https://mlsanity.com/paper/2205.11438/) (2022)
- [Syntax-semantics interface: an algebraic model](https://mlsanity.com/paper/2311.06189/) (2023)
- [Generating Harder Cross-document Event Coreference Resolution Datasets using Metaphoric Paraphrasing](https://mlsanity.com/paper/2407.11988/) (2024)
- [A Rationale-centric Counterfactual Data Augmentation Method for Cross-Document Event Coreference Resolution](https://mlsanity.com/paper/2404.01921/) (2024)
- [Exploring Multiple Strategies to Improve Multilingual Coreference Resolution in CorefUD](https://mlsanity.com/paper/2408.16893/) (2024)
- [Context-based Ontology Modelling for Database: Enabling ChatGPT for Semantic Database Management](https://mlsanity.com/paper/2303.07351/) (2023)
- [Generalizable and Stable Finetuning of Pretrained Language Models on Low-Resource Texts](https://mlsanity.com/paper/2403.12918/) (2024)
- [The syntax-semantics interface in a child's path: A study of 3- to 11-year-olds' elicited production of Mandarin recursive relative clauses](https://mlsanity.com/paper/2406.04025/) (2024)
- [EasyECR: A Library for Easy Implementation and Evaluation of Event Coreference Resolution Models](https://mlsanity.com/paper/2406.14106/) (2024)
- [Harnessing the Intrinsic Knowledge of Pretrained Language Models for Challenging Text Classification Settings](https://mlsanity.com/paper/2408.15650/) (2024)

## Built on by (most cited first, 1 of 1)

- [Human-Like Anaphor Resolution in Large Language Models](https://mlsanity.com/paper/2608.05630/) (2026)

## Abstract (arXiv)

Large language models have made significant advancements in various natural language processing tasks, including coreference resolution. However, traditional methods often fall short in effectively distinguishing referential relationships due to a lack of integration between syntactic and semantic information. This study introduces an innovative framework aimed at enhancing coreference resolution by utilizing pretrained language models. Our approach combines syntax parsing with semantic role labeling to accurately capture finer distinctions in referential relationships. By employing state-of-the-art pretrained models to gather contextual embeddings and applying an attention mechanism for fine-tuning, we improve the performance of coreference tasks. Experimental results across diverse datasets show that our method surpasses conventional coreference resolution systems, achieving notable accuracy in disambiguating references. This development not only improves coreference resolution outcomes but also positively impacts other natural language processing tasks that depend on precise referential understanding.