Named Entity Recognition in COVID-19 tweets with Entity Knowledge Augmentation
Abstract: The COVID-19 pandemic causes severe social and economic disruption around the world, raising various subjects that are discussed or argued over on social media. Identifying pandemic-related named entities as expressed on social media is fundamental and important for understanding the discussions on the pandemic. However, there is limited work on named entity recognition on this topic due to the following challenges: 1) annotated data is rare and insufficient to train a robust recognition model, and 2) named entity recognition in COVID-19 requires extensive knowledge of the pandemic. To address this, we propose a novel entity knowledge augmentation for named entity recognition systems in COVID-19 tweets. Experiments carried out on the COVID-19 tweets dataset show that our proposed entity knowledge augmentation improves NER performance, achieving an F1 score of 84.10.
Paper Type: Short
Research Area: Information Extraction
Research Area Keywords: named entity recognition and relation extraction, knowledge augmentation, covid-19 tweets, pre-trained language model
Contribution Types: NLP engineering experiment
Languages Studied: English
Submission Number: 4960
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