Overview of NLPCC 2023 Shared Task 6: Chinese Few-Shot and Zero-Shot Entity Linking.

Entity Linking (EL) is the task of grounding a textual mention in context to a corresponding entity in a knowledge base. However, current EL systems demonstrate a popularity bias, significantly underperforming on tail and emerging entities. To this end, we organize NLPCC 2023 Shared Task 6, i.e., Chinese Few-shot and Zero-shot Entity Linking, which aims at testing the generalization ability of Chinese EL systems to less popular and newly emerging entities. The dataset for this task is a human-calibrated and multi-domain Chinese EL benchmark with Wikidata as KB, consisting of few-shot and zero-shot test sets. There are 22 registered teams and 13 submissions in total, and the highest accuracy is 0.6915. The submitted approaches focus on different aspects of this problem and use diverse techniques to boost the performance. All relevant information can be found at https://github.com/HITsz-TMG/Hansel/tree/main/NLPCC .