论文标题
Se-NLP在Semeval-2022任务11：复杂的命名实体识别，并链接实体链接
SU-NLP at SemEval-2022 Task 11: Complex Named Entity Recognition with Entity Linking
论文作者
论文摘要
本文介绍了Sabancı大学自然语言处理小组在Semeval-2022多核代理人任务中提出的系统。我们开发了一个无监督的实体链接管道，该实体在Wikipedia的帮助下检测潜在实体提及，还使用相应的Wikipedia上下文来帮助分类器找到该提及的命名实体类型。我们的结果表明，我们的管道大大提高了性能，尤其是对于低外观设置中的复杂实体。
This paper describes the system proposed by Sabancı University Natural Language Processing Group in the SemEval-2022 MultiCoNER task. We developed an unsupervised entity linking pipeline that detects potential entity mentions with the help of Wikipedia and also uses the corresponding Wikipedia context to help the classifier in finding the named entity type of that mention. Our results showed that our pipeline improved performance significantly, especially for complex entities in low-context settings.
