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CLIMATELI: Evaluating Entity Linking on Climate Change Data

MCML Authors

Shijia Zhou

→ Group Barbara Plank
AI and Computational Linguistics

Siyao Peng

Dr.

→ Group Barbara Plank
AI and Computational Linguistics

Barbara Plank

Prof. Dr.

Principal Investigator

AI and Computational Linguistics

Abstract

Climate Change (CC) is a pressing topic of global importance, attracting increasing attention across research fields, from social sciences to Natural Language Processing (NLP). CC is also discussed in various settings and communication platforms, from academic publications to social media forums. Understanding who and what is mentioned in such data is a first critical step to gaining new insights into CC. We present CLIMATELI (CLIMATe Entity LInking), the first manually annotated CC dataset that links 3,087 entity spans to Wikipedia. Using CLIMATELI (CLIMATe Entity LInking), we evaluate existing entity linking (EL) systems on the CC topic across various genres and propose automated filtering methods for CC entities. We find that the performance of EL models notably lags behind humans at both token and entity levels. Testing within the scope of retaining or excluding non-nominal and/or non-CC entities particularly impacts the models’ performances.

inproceedings

ClimateNLP @ACL 2024

1st Workshop on Natural Language Processing Meets Climate Change at the 62nd Annual Meeting of the Association for Computational Linguistics. Bangkok, Thailand, Aug 11-16, 2024.