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        <title>WikiGUM: Exhaustive Entity Linking for Wikification in 12 Genres</title>
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    <name type="personal">
        <namePart type="given">Jessica</namePart>
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            <title>Proceedings of the Joint 15th Linguistic Annotation Workshop (LAW) and 3rd Designing Meaning Representations (DMR) Workshop</title>
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        <name type="personal">
            <namePart type="given">Claire</namePart>
            <namePart type="family">Bonial</namePart>
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            <namePart type="given">Nianwen</namePart>
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    <abstract>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.</abstract>
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    <identifier type="doi">10.18653/v1/2021.law-1.18</identifier>
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%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

Markdown (Informal)

[WikiGUM: Exhaustive Entity Linking for Wikification in 12 Genres](https://aclanthology.org/2021.law-1.18) (Lin & Zeldes, LAW 2021)

• WikiGUM: Exhaustive Entity Linking for Wikification in 12 Genres (Lin & Zeldes, LAW 2021)

ACL

• Jessica Lin and Amir Zeldes. 2021. WikiGUM: Exhaustive Entity Linking for Wikification in 12 Genres. In Proceedings of the Joint 15th Linguistic Annotation Workshop (LAW) and 3rd Designing Meaning Representations (DMR) Workshop, pages 170–175, Punta Cana, Dominican Republic. Association for Computational Linguistics.