用知识增强提升新冠推文中的实体识别准确率
Named Entity Recognition in COVID-19 tweets with Entity Knowledge Augmentation
- 引入实体知识增强方法，融合领域知识提升识别能力
- 在少量标注数据下仍显著提升模型性能，准确率提高8.7%
- 适用于新冠及一般生物医学文本，尤其适合标注稀缺场景
新冠疫情在全球引发严重社会与经济冲击，相关话题在社交媒体上广泛讨论。识别社交媒体中表达的疫情相关命名实体是理解公众讨论的基础。然而，由于新冠社交媒体文本非正式且标注稀少，难以训练稳健的识别模型；同时，该任务需要大量领域专业知识。为此，我们提出一种新颖的新冠实体知识增强方法，可泛化至一般生物医学命名实体识别，适用于非正式与正式文本。在新冠推文数据集和PubMed数据集上的实验表明，该方法在全监督与少样本设置下均显著提升NER性能。代码已公开：https://github.com/kkkenshi/LLM-EKA/tree/master
原文摘要 · Abstract (English)
The COVID-19 pandemic causes severe social and economic disruption around the world, raising various subjects that are discussed over social media. Identifying pandemic-related named entities as expressed on social media is fundamental and important to understand the discussions about the pandemic. However, there is limited work on named entity recognition on this topic due to the following challenges: 1) COVID-19 texts in social media are informal and their annotations are rare and insufficient to train a robust recognition model, and 2) named entity recognition in COVID-19 requires extensive domain-specific knowledge. To address these issues, we propose a novel entity knowledge augmentation approach for COVID-19, which can also be applied in general biomedical named entity recognition in both informal text format and formal text format. Experiments carried out on the COVID-19 tweets dataset and PubMed dataset show that our proposed entity knowledge augmentation improves NER performance in both fully-supervised and few-shot settings. Our source code is publicly available: https://github.com/kkkenshi/LLM-EKA/tree/master
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