A step further towards a consensus on linking tweets to Wikipedia

Nait-Hamoud, Mohamed Cherif; Lahfa, Fedoua; Ennaji, Abdellatif

doi:10.1007/s12065-020-00549-8

Cited by 1 publication

(1 citation statement)

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“…GERBIL (Röder et al, 2018) standardizes ED evaluation over multiple datasets in a unifying framework, but does not define the training data and thus only focuses on comparing already-trained models. Similarly, a range of prior works have sought to refine and standardize ED evaluation (Waitelonis et al, 2019;Nait-Hamoud et al, 2021;Noullet et al, 2021;Odoni et al, 2019;van Erp and Groth, 2020;Braşoveanu et al, 2018). In contrast, ZELDA defines the full experimental setup, including training data, the entity vocabulary and other training signals.…”

Section: Related Workmentioning

confidence: 99%

ZELDA: A Comprehensive Benchmark for Supervised Entity Disambiguation

Milich,

Akbik

2023

Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

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Entity disambiguation (ED) is the task of disambiguating named entity mentions in text to unique entries in a knowledge base. Due to its industrial relevance, as well as current progress in leveraging pre-trained language models, a multitude of ED approaches have been proposed in recent years. However, we observe a severe lack of uniformity across experimental setups in current ED work, rendering a direct comparison of approaches based solely on reported numbers impossible: Current approaches widely differ in the data set used to train, the size of the covered entity vocabulary, and the usage of additional signals such as candidate lists. To address this issue, we present ZELDA, a novel entity disambiguation benchmark that includes a unified training data set, entity vocabulary, candidate lists, as well as challenging evaluation splits covering 8 different domains. We illustrate its design and construction, and present experiments in which we train and compare current state-of-the-art approaches on our benchmark. To encourage greater direct comparability in the entity disambiguation domain, we open source our benchmark at https: //github.com/flairNLP/zelda.

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