Abstract
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.
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Notes
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To facilitate searching, we provide annotators with pre-filled search query templates in an annotation tool, such as Google queries with entity names and target domains.
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Acknowledgement
This work is jointly supported by grants: This work is jointly supported by grants: Natural Science Foundation of China (No. 62006061 and 82171475), Strategic Emerging Industry Development Special Funds of Shenzhen (No.JCYJ20200109113403826).
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Xu, Z., Shan, Z., Hu, B., Zhang, M. (2023). Overview of NLPCC 2023 Shared Task 6: Chinese Few-Shot and Zero-Shot Entity Linking. In: Liu, F., Duan, N., Xu, Q., Hong, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2023. Lecture Notes in Computer Science(), vol 14304. Springer, Cham. https://doi.org/10.1007/978-3-031-44699-3_23
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