USTC-NELSLIP 参与 SemEval-2023 任务2：面向多语言复杂 NER 的地名词典统计构建与双适配
USTC-NELSLIP at SemEval-2023 Task 2: Statistical Construction and Dual Adaptation of Gazetteer for Multilingual Complex NER
AI总结：
针对多语言复杂命名实体识别任务，提出SCDAG方法，通过统计构建地名词典并在句级和实体级双适配地名词典网络与XLM-R，在SemEval-2023任务2印地语赛道获第一名。
摘要：
本文介绍了USTC-NELSLIP团队为SemEval-2023任务2多语言复杂命名实体识别（MultiCoNER II）开发的系统。针对多语言复杂NER，提出了名为Statistical Construction and Dual Adaptation of Gazetteer（SCDAG，地名词典统计构建与双适配）的方法。该方法首先利用基于统计的方法构建地名词典；其次，通过在句子层面和实体层面最小化两者之间的KL散度，对地名词典网络和语言模型的表示进行适配；最后，将这两个网络整合用于有监督命名实体识别（NER）训练。所提方法应用于XLM-R模型，搭配从Wikidata构建的地名词典，在不同赛道上展现出极强的泛化能力。实验结果与详细分析验证了该方法的有效性。官方结果显示，我们的系统在该任务的一个赛道（印地语）上排名第一。
英文摘要：
This paper describes the system developed by the USTC-NELSLIP team for SemEval-2023 Task 2 Multilingual Complex Named Entity Recognition (MultiCoNER II). A method named Statistical Construction and Dual Adaptation of Gazetteer (SCDAG) is proposed for Multilingual Complex NER. The method first utilizes a statistics-based approach to construct a gazetteer. Secondly, the representations of gazetteer networks and language models are adapted by minimizing the KL divergence between them at both the sentence-level and entity-level. Finally, these two networks are then integrated for supervised named entity recognition (NER) training. The proposed method is applied to XLM-R with a gazetteer built from Wikidata, and shows great generalization ability across different tracks. Experimental results and detailed analysis verify the effectiveness of the proposed method. The official results show that our system ranked 1st on one track (Hindi) in this task.
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。
