Association for Computational Linguistics. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6982–6993, Online. From Zero to Hero: Human-In-The-Loop Entity Linking in Low Resource Domains. Anthology ID: 2020.acl-main.624 Volume: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics Month: July Year: 2020 Address: Online Venue: ACL SIG: Publisher: Association for Computational Linguistics Note: Pages: 6982–6993 Language: URL: DOI: 10.18653/v1/2020.acl-main.624 Bibkey: klie-etal-2020-zero Cite (ACL): Jan-Christoph Klie, Richard Eckart de Castilho, and Iryna Gurevych. An open-source and ready-to-use implementation based on the text annotation platform INCEpTION () is made available. In a user study, the annotation speed improves by 35% compared to annotating without interactive support users report that they strongly prefer our system. We evaluate our ranking approach in a simulation on difficult texts and show that it greatly outperforms a strong baseline in ranking accuracy. ![]() 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. ![]() However, in the above scenario, there exists hardly annotated data, and it needs to be created from scratch. ![]() Existing approaches are mostly inappropriate for this, as they depend on training data. The use of EL in such domains requires handling noisy texts, low resource settings and domain-specific KBs. 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. Abstract Entity linking (EL) is concerned with disambiguating entity mentions in a text against knowledge bases (KB).
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