%0 Conference Proceedings %T WikiGUM: Exhaustive Entity Linking for Wikification in 12 Genres %A Lin, Jessica %A Zeldes, Amir %Y Bonial, Claire %Y Xue, Nianwen %S Proceedings of the Joint 15th Linguistic Annotation Workshop (LAW) and 3rd Designing Meaning Representations (DMR) Workshop %D 2021 %8 November %I Association for Computational Linguistics %C Punta Cana, Dominican Republic %F lin-zeldes-2021-wikigum %X 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. %R 10.18653/v1/2021.law-1.18 %U https://aclanthology.org/2021.law-1.18 %U https://doi.org/10.18653/v1/2021.law-1.18 %P 170-175