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.

通过 CLIMATELI 数据集，评估现有的实体链接模型在气候变化主题中的表现，并提出用于气候变化实体的自动筛选方法

CLIMATELI: 评估气候变化数据中的实体链接

CLIMATELI: Evaluating Entity Linking on Climate Change Data

In our continuously evolving world, entities change over time and new,
previously non-existing or unknown, entities appear. We study how this
evolutionary scenario impacts the performance on a well established entity
linking (EL) task. For that study, we introduce TempEL, an entity linking
dataset that consists of time-stratified English Wikipedia snapshots from 2013
to 2022, from which we collect both anchor mentions of entities, and these
target entities' descriptions. By capturing such temporal aspects, our newly
introduced TempEL resource contrasts with currently existing entity linking
datasets, which are composed of fixed mentions linked to a single static
version of a target Knowledge Base (e.g., Wikipedia 2010 for CoNLL-AIDA).
Indeed, for each of our collected temporal snapshots, TempEL contains links to
entities that are continual, i.e., occur in all of the years, as well as
completely new entities that appear for the first time at some point. Thus, we
enable to quantify the performance of current state-of-the-art EL models for:
(i) entities that are subject to changes over time in their Knowledge Base
descriptions as well as their mentions' contexts, and (ii) newly created
entities that were previously non-existing (e.g., at the time the EL model was
trained). Our experimental results show that in terms of temporal performance
degradation, (i) continual entities suffer a decrease of up to 3.1% EL
accuracy, while (ii) for new entities this accuracy drop is up to 17.9%. This
highlights the challenge of the introduced TempEL dataset and opens new
research prospects in the area of time-evolving entity disambiguation.

本文介绍了一个新的实体链接 (TempEL) 数据集，可以量化当前最先进的 EL 模型在知识库描述和提及环境中发生时间变化的实体上的性能表现以及那些以前不存在的新实体的实体链接性能，并发现 (在 temporal snapshots 中) 不断存在的实体在 EL 精度方面下降了 3.1％，而新实体的下降高达 17.9％，这强调了时间演变实体消岐的面临的挑战和新的研究前景。