# WikiGUM: Exhaustive Entity Linking for Wikification in 12 Genres

arXiv: https://arxiv.org/abs/2109.07449 · HTML: https://mlsanity.com/paper/2109.07449/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 15 Sep 2021.

## Builds on (most cited first, 4 of 4)

- [End-to-End Neural Entity Linking](https://mlsanity.com/paper/1808.07699/) (2018)
- [NNE: A Dataset for Nested Named Entity Recognition in English Newswire](https://mlsanity.com/paper/1906.01359/) (2019)
- [Analysis of Named Entity Recognition and Linking for Tweets](https://mlsanity.com/paper/1410.7182/) (2014)
- [Fast End-to-End Wikification](https://mlsanity.com/paper/1908.06785/) (2019)

## Built on by (most cited first, 9 of 9)

- [GENTLE: A Genre-Diverse Multilayer Challenge Set for English NLP and Linguistic Evaluation](https://mlsanity.com/paper/2306.01966/) (2023)
- [GUM-SAGE: A Novel Dataset and Approach for Graded Entity Salience Prediction](https://mlsanity.com/paper/2504.10792/) (2025)
- [Can we Fix the Scope for Coreference? Problems and Solutions for Benchmarks beyond OntoNotes](https://mlsanity.com/paper/2112.09742/) (2021)
- [Subjectivity in the Annotation of Bridging Anaphora](https://mlsanity.com/paper/2506.07297/) (2025)
- [What's Hard in English RST Parsing? Predictive Models for Error Analysis](https://mlsanity.com/paper/2309.04940/) (2023)
- [ChuLo: Chunk-Level Key Information Representation for Long Document Understanding](https://mlsanity.com/paper/2410.11119/) (2024)
- [GUMBridge: a Corpus for Varieties of Bridging Anaphora](https://mlsanity.com/paper/2512.07134/) (2025)
- [CLIMATELI: Evaluating Entity Linking on Climate Change Data](https://mlsanity.com/paper/2406.16732/) (2024)
- [Expect the Unexpected? Testing the Surprisal of Salient Entities](https://mlsanity.com/paper/2604.10724/) (2026)

## Abstract (arXiv)

Previous work on Entity Linking has focused on resources targeting non-nested proper named entity mentions, often in data from Wikipedia, i.e. Wikification. In this paper, we present and evaluate WikiGUM, a fully wikified dataset, covering all mentions of named entities, including their non-named and pronominal mentions, as well as mentions nested within other mentions. The dataset covers a broad range of 12 written and spoken genres, most of which have not been included in Entity Linking efforts to date, leading to poor performance by a pretrained SOTA system in our evaluation. The availability of a variety of other annotations for the same data also enables further research on entities in context.