Speech Entity Linking aims to recognize and disambiguate named entities in
spoken languages. Conventional methods suffer gravely from the unfettered
speech styles and the noisy transcripts generated by ASR systems. In this
paper, we propose a novel approach called Knowledge Enhanced Named Entity
Recognition (KENER), which focuses on improving robustness through painlessly
incorporating proper knowledge in the entity recognition stage and thus
improving the overall performance of entity linking. KENER first retrieves
candidate entities for a sentence without mentions, and then utilizes the
entity descriptions as extra information to help recognize mentions. The
candidate entities retrieved by a dense retrieval module are especially useful
when the input is short or noisy. Moreover, we investigate various data
sampling strategies and design effective loss functions, in order to improve
the quality of retrieved entities in both recognition and disambiguation
stages. Lastly, a linking with filtering module is applied as the final
safeguard, making it possible to filter out wrongly-recognized mentions. Our
system achieves 1st place in Track 1 and 2nd place in Track 2 of NLPCC-2022
Shared Task 2.

该研究提出了一种基于知识增强的命名实体识别方法（KENER），该方法利用密集检索模块来识别候选实体，利用该实体的描述作为额外信息来帮助识别提及，并研究了多种数据抽样策略和设计了有效的损失函数。该系统在 NLPCC-2022 共享任务 2 的 Track 1 中获得第一名，在 Track 2 中获得第二名。