WikiGUM: Exhaustive Entity Linking for Wikification in 12 Genres
This provides a comprehensive dataset for entity linking research, addressing gaps in genre coverage and mention types, though it is incremental as it builds on existing wikification efforts.
The paper tackles the problem of limited entity linking resources by introducing WikiGUM, a fully wikified dataset covering all mentions of named entities, including non-named and nested mentions, across 12 genres, and shows that a pretrained SOTA system performs poorly on it.
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.