论文标题
Duth在2020年Semeval-2020任务11：带有实体映射的BERT进行宣传分类
DUTH at SemEval-2020 Task 11: BERT with Entity Mapping for Propaganda Classification
论文作者
论文摘要
该报告描述了Democtus of Thrace大学（DUTH）参与Semeval-2020任务11：在新闻文章中检测宣传技术的方法。我们的团队处理子任务2：技术分类。我们使用浅层自然语言处理（NLP）预处理技术来减少数据集中的噪声，特征选择方法和常见的监督机器学习算法。我们的最终模型基于使用实体映射的BERT系统。为了提高模型的准确性，我们通过采用单词类和实体识别来将某些单词映射为五个不同的类别。
This report describes the methods employed by the Democritus University of Thrace (DUTH) team for participating in SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles. Our team dealt with Subtask 2: Technique Classification. We used shallow Natural Language Processing (NLP) preprocessing techniques to reduce the noise in the dataset, feature selection methods, and common supervised machine learning algorithms. Our final model is based on using the BERT system with entity mapping. To improve our model's accuracy, we mapped certain words into five distinct categories by employing word-classes and entity recognition.
