arXiv 2308.033652023-08-13cs.CL

利用由粗到细的基于词典的检索器改进少样本与零样本实体链接

Improving Few-shot and Zero-shot Entity Linking with Coarse-to-Fine Lexicon-based Retriever

Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies（广东省新型安全智能技术重点实验室）；Central South University（中南大学）

Shijue Huang, Bingbing Wang, Libo Qin, Qin Zhao, Ruifeng Xu

备注：: Accepted to NLPCC2023

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AI总结：

针对少样本和零样本实体链接，本文提出由实体名称到描述的两层由粗到细词典检索器，在无需大量微调的情况下取得优异性能并获得 NLPCC 2023 共享任务第一名。

摘要：

少样本和零样本实体链接聚焦于尾部实体与新兴实体，它们更具挑战性，但也更贴近真实世界场景。主流方法是“检索并重排序”两阶段框架。本文提出一种由粗到细的基于词典的检索器，以有效方式检索实体候选，该检索器分两层运行。第一层利用实体名称检索粗粒度候选，第二层则在粗粒度候选中将搜索范围缩小到细粒度候选。此外，第二层利用实体描述，有效消除与现有热门实体同名的尾部实体或新实体的歧义。实验结果表明，我们的方法无需在检索阶段进行大量微调即可获得优越性能。值得注意的是，我们的方法在 NLPCC 2023 Shared Task 6 中文少样本与零样本实体链接任务中排名第一。

英文摘要：

Few-shot and zero-shot entity linking focus on the tail and emerging entities, which are more challenging but closer to real-world scenarios. The mainstream method is the ''retrieve and rerank'' two-stage framework. In this paper, we propose a coarse-to-fine lexicon-based retriever to retrieve entity candidates in an effective manner, which operates in two layers. The first layer retrieves coarse-grained candidates by leveraging entity names, while the second layer narrows down the search to fine-grained candidates within the coarse-grained ones. In addition, this second layer utilizes entity descriptions to effectively disambiguate tail or new entities that share names with existing popular entities. Experimental results indicate that our approach can obtain superior performance without requiring extensive finetuning in the retrieval stage. Notably, our approach ranks the 1st in NLPCC 2023 Shared Task 6 on Chinese Few-shot and Zero-shot Entity Linking.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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