Named Entity Recognition in COVID-19 tweets with Entity Knowledge Augmentation
This work addresses the challenge of identifying pandemic-related entities in informal social media text, which is important for understanding COVID-19 discussions, but it is incremental as it builds on existing NER methods with domain-specific enhancements.
The paper tackled the problem of named entity recognition in COVID-19 tweets by proposing an entity knowledge augmentation approach, which improved NER performance on both COVID-19 tweets and PubMed datasets in fully-supervised and few-shot settings.
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