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        <title>From Zero to Hero: Human-In-The-Loop Entity Linking in Low Resource Domains</title>
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    <name type="personal">
        <namePart type="given">Jan-Christoph</namePart>
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            <namePart type="given">Dan</namePart>
            <namePart type="family">Jurafsky</namePart>
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    <abstract>Entity linking (EL) is concerned with disambiguating entity mentions in a text against knowledge bases (KB). It is crucial in a considerable number of fields like humanities, technical writing and biomedical sciences to enrich texts with semantics and discover more knowledge. The use of EL in such domains requires handling noisy texts, low resource settings and domain-specific KBs. Existing approaches are mostly inappropriate for this, as they depend on training data. However, in the above scenario, there exists hardly annotated data, and it needs to be created from scratch. We therefore present a novel domain-agnostic Human-In-The-Loop annotation approach: we use recommenders that suggest potential concepts and adaptive candidate ranking, thereby speeding up the overall annotation process and making it less tedious for users. We evaluate our ranking approach in a simulation on difficult texts and show that it greatly outperforms a strong baseline in ranking accuracy. In a user study, the annotation speed improves by 35% compared to annotating without interactive support; users report that they strongly prefer our system. An open-source and ready-to-use implementation based on the text annotation platform INCEpTION (https://inception-project.github.io) is made available.</abstract>
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%0 Conference Proceedings
%T From Zero to Hero: Human-In-The-Loop Entity Linking in Low Resource Domains
%A Klie, Jan-Christoph
%A Eckart de Castilho, Richard
%A Gurevych, Iryna
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F klie-etal-2020-zero
%X Entity linking (EL) is concerned with disambiguating entity mentions in a text against knowledge bases (KB). It is crucial in a considerable number of fields like humanities, technical writing and biomedical sciences to enrich texts with semantics and discover more knowledge. The use of EL in such domains requires handling noisy texts, low resource settings and domain-specific KBs. Existing approaches are mostly inappropriate for this, as they depend on training data. However, in the above scenario, there exists hardly annotated data, and it needs to be created from scratch. We therefore present a novel domain-agnostic Human-In-The-Loop annotation approach: we use recommenders that suggest potential concepts and adaptive candidate ranking, thereby speeding up the overall annotation process and making it less tedious for users. We evaluate our ranking approach in a simulation on difficult texts and show that it greatly outperforms a strong baseline in ranking accuracy. In a user study, the annotation speed improves by 35% compared to annotating without interactive support; users report that they strongly prefer our system. An open-source and ready-to-use implementation based on the text annotation platform INCEpTION (https://inception-project.github.io) is made available.
%R 10.18653/v1/2020.acl-main.624
%U https://aclanthology.org/2020.acl-main.624/
%U https://doi.org/10.18653/v1/2020.acl-main.624
%P 6982-6993

Markdown (Informal)

[From Zero to Hero: Human-In-The-Loop Entity Linking in Low Resource Domains](https://aclanthology.org/2020.acl-main.624/) (Klie et al., ACL 2020)

• From Zero to Hero: Human-In-The-Loop Entity Linking in Low Resource Domains (Klie et al., ACL 2020)

ACL

• Jan-Christoph Klie, Richard Eckart de Castilho, and Iryna Gurevych. 2020. From Zero to Hero: Human-In-The-Loop Entity Linking in Low Resource Domains. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6982–6993, Online. Association for Computational Linguistics.