WikiGUM: Exhaustive Entity Linking for Wikification in 12 Genres

EMNLP (LAW, DMR) 2021  ·  Jessica Lin, Amir Zeldes ·

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.

PDF Abstract EMNLP (LAW, 2021 PDF EMNLP (LAW, 2021 Abstract
No code implementations yet. Submit your code now

Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Entity Linking GUM baseline F1 26.4 # 1

Methods


No methods listed for this paper. Add relevant methods here