From Zero to Hero: Human-In-The-Loop Entity Linking in Low Resource Domains

Jan-Christoph Klie, Richard Eckart de Castilho, Iryna Gurevych


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.
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:
https://www.aclweb.org/anthology/2020.acl-main.624
DOI:
10.18653/v1/2020.acl-main.624
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.acl-main.624.pdf
Video:
 http://slideslive.com/38929059