# GUMBridge: a Corpus for Varieties of Bridging Anaphora

arXiv: https://arxiv.org/abs/2512.07134 · HTML: https://mlsanity.com/paper/2512.07134/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 33 references that are in the map.
- First posted on arXiv 8 Dec 2025.

## Builds on (most cited first, 6 of 6)

- [GENTLE: A Genre-Diverse Multilayer Challenge Set for English NLP and Linguistic Evaluation](https://mlsanity.com/paper/2306.01966/) (2023)
- [OntoGUM: Evaluating Contextualized SOTA Coreference Resolution on 12 More Genres](https://mlsanity.com/paper/2106.00933/) (2021)
- [Bridging Anaphora Resolution as Question Answering](https://mlsanity.com/paper/2004.07898/) (2020)
- [WikiGUM: Exhaustive Entity Linking for Wikification in 12 Genres](https://mlsanity.com/paper/2109.07449/) (2021)
- [Subjectivity in the Annotation of Bridging Anaphora](https://mlsanity.com/paper/2506.07297/) (2025)
- [Unifying the Scope of Bridging Anaphora Types in English: Bridging Annotations in ARRAU and GUM](https://mlsanity.com/paper/2410.01170/) (2024)

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

- [LLMBridge: An LLM Pipeline for End-to-end Referential Bridging Resolution in English](https://mlsanity.com/paper/2605.29048/) (2026)

## Abstract (arXiv)

Bridging is an anaphoric phenomenon where the referent of an entity in a discourse is dependent on a previous, non-identical entity for interpretation, such as in "There is 'a house'. 'The door' is red," where the door is specifically understood to be the door of the aforementioned house. While there are several existing resources in English for bridging anaphora, most are small, provide limited coverage of the phenomenon, and/or provide limited genre coverage. In this paper, we introduce GUMBridge, a new resource for bridging, which includes 16 diverse genres of English, providing both broad coverage for the phenomenon and granular annotations for the subtype categorization of bridging varieties. We also present an evaluation of annotation quality and report on baseline performance using open and closed source contemporary LLMs on three tasks underlying our data, showing that bridging resolution and subtype classification remain difficult NLP tasks in the age of LLMs.