# Using Type Information to Improve Entity Coreference Resolution

arXiv: https://arxiv.org/abs/2010.05738 · HTML: https://mlsanity.com/paper/2010.05738/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2020.

## Built on by (most cited first, 6 of 6)

- [Modeling Fine-Grained Entity Types with Box Embeddings](https://mlsanity.com/paper/2101.00345/) (2021)
- [TABi: Type-Aware Bi-Encoders for Open-Domain Entity Retrieval](https://mlsanity.com/paper/2204.08173/) (2022)
- [Evaluating the Impact of a Hierarchical Discourse Representation on Entity Coreference Resolution Performance](https://mlsanity.com/paper/2104.10215/) (2021)
- [Review of coreference resolution in English and Persian](https://mlsanity.com/paper/2211.04428/) (2022)
- [Persian Pronoun Resolution: Leveraging Neural Networks and Language Models](https://mlsanity.com/paper/2405.10714/) (2024)
- [Interpretable Coreference Resolution Evaluation Using Explicit Semantics](https://mlsanity.com/paper/2605.10627/) (2026)

## Abstract (arXiv)

Coreference resolution (CR) is an essential part of discourse analysis. Most recently, neural approaches have been proposed to improve over SOTA models from earlier paradigms. So far none of the published neural models leverage external semantic knowledge such as type information. This paper offers the first such model and evaluation, demonstrating modest gains in accuracy by introducing either gold standard or predicted types. In the proposed approach, type information serves both to (1) improve mention representation and (2) create a soft type consistency check between coreference candidate mentions. Our evaluation covers two different grain sizes of types over four different benchmark corpora.