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    <titleInfo>
        <title>Entity Embedding Completion for Wide-Coverage Entity Disambiguation</title>
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    <name type="personal">
        <namePart type="given">Daisuke</namePart>
        <namePart type="family">Oba</namePart>
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    <name type="personal">
        <namePart type="given">Ikuya</namePart>
        <namePart type="family">Yamada</namePart>
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    <name type="personal">
        <namePart type="given">Naoki</namePart>
        <namePart type="family">Yoshinaga</namePart>
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    <name type="personal">
        <namePart type="given">Masashi</namePart>
        <namePart type="family">Toyoda</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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            <title>Findings of the Association for Computational Linguistics: EMNLP 2022</title>
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            <namePart type="given">Yoav</namePart>
            <namePart type="family">Goldberg</namePart>
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            <namePart type="given">Zornitsa</namePart>
            <namePart type="family">Kozareva</namePart>
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        <name type="personal">
            <namePart type="given">Yue</namePart>
            <namePart type="family">Zhang</namePart>
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    <abstract>Entity disambiguation (ED) is typically solved by learning to classify a given mention into one of the entities in the model‘s entity vocabulary by referring to their embeddings. However, this approach cannot address mentions of entities that are not covered by the entity vocabulary. Aiming to enhance the applicability of ED models, we propose a method of extending a state-of-the-art ED model by dynamically computing embeddings of out-of-vocabulary entities. Specifically, our method computes embeddings from entity descriptions and mention contexts. Experiments with standard benchmark datasets show that the extended model performs comparable to or better than existing models whose entity embeddings are trained for all candidate entities as well as embedding-free models. We release our source code and model checkpoints at https://github.com/studio-ousia/steel.</abstract>
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    <identifier type="doi">10.18653/v1/2022.findings-emnlp.472</identifier>
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        <date>2022-12</date>
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%0 Conference Proceedings
%T Entity Embedding Completion for Wide-Coverage Entity Disambiguation
%A Oba, Daisuke
%A Yamada, Ikuya
%A Yoshinaga, Naoki
%A Toyoda, Masashi
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F oba-etal-2022-entity
%X Entity disambiguation (ED) is typically solved by learning to classify a given mention into one of the entities in the model‘s entity vocabulary by referring to their embeddings. However, this approach cannot address mentions of entities that are not covered by the entity vocabulary. Aiming to enhance the applicability of ED models, we propose a method of extending a state-of-the-art ED model by dynamically computing embeddings of out-of-vocabulary entities. Specifically, our method computes embeddings from entity descriptions and mention contexts. Experiments with standard benchmark datasets show that the extended model performs comparable to or better than existing models whose entity embeddings are trained for all candidate entities as well as embedding-free models. We release our source code and model checkpoints at https://github.com/studio-ousia/steel.
%R 10.18653/v1/2022.findings-emnlp.472
%U https://aclanthology.org/2022.findings-emnlp.472/
%U https://doi.org/10.18653/v1/2022.findings-emnlp.472
%P 6333-6344

Markdown (Informal)

[Entity Embedding Completion for Wide-Coverage Entity Disambiguation](https://aclanthology.org/2022.findings-emnlp.472/) (Oba et al., Findings 2022)

• Entity Embedding Completion for Wide-Coverage Entity Disambiguation (Oba et al., Findings 2022)

ACL

• Daisuke Oba, Ikuya Yamada, Naoki Yoshinaga, and Masashi Toyoda. 2022. Entity Embedding Completion for Wide-Coverage Entity Disambiguation. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 6333–6344, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.