@inproceedings{779d7cb4184d49d0af021ddcaefc21b1,
title = "Overview of NLPCC 2023 Shared Task 6: Chinese Few-Shot and Zero-Shot Entity Linking",
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.",
keywords = "Entity Linking, Few-shot Learning, Zero-shot Learning",
author = "Zhenran Xu and Zifei Shan and Baotian Hu and Min Zhang",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 12th National CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2023 ; Conference date: 12-10-2023 Through 15-10-2023",
year = "2023",
doi = "10.1007/978-3-031-44699-3\_23",
language = "英语",
isbn = "9783031446986",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "257--265",
editor = "Fei Liu and Nan Duan and Qingting Xu and Yu Hong",
booktitle = "Natural Language Processing and Chinese Computing - 12th National CCF Conference, NLPCC 2023, Proceedings",
address = "德国",
}