NER - VLSP 2021: A Span-Based Model for Named Entity Recognition Task with Co-teaching+ Training Strategy

Pham Hoai Phu Thinh, Vu Tran Duy, Do Tran Anh Duc
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Abstract

Named entities containing other named entities inside are referred to as nested entities, which commonly exist in news articles and other documents. However, most studies in the field of Vietnamese named entity recognition entirely ignore nested entities. In this report, we describe our system at VLSP 2021 evaluation campaign, adopting the technique from dependency parsing to tackle the problem of nested entities. We also apply Coteaching+ technique to enhance the overall performance and propose an ensemble algorithm to combine predictions. Experimental results show that the ensemble method achieves the best F1 score on the test set at VLSP 2021.
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NER - VLSP 2021:基于跨域的协同教学+训练策略命名实体识别任务模型
包含其他命名实体的命名实体被称为嵌套实体,通常存在于新闻文章和其他文档中。然而,大多数越南语命名实体识别领域的研究完全忽略了嵌套实体。在本报告中,我们在VLSP 2021评估活动中描述了我们的系统,采用依赖解析技术来解决嵌套实体的问题。我们还应用了Coteaching+技术来提高整体性能,并提出了一种集成算法来组合预测。实验结果表明,该方法在VLSP 2021的测试集上获得了最佳的F1分数。
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