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Combining BERT with Contextual Linguistic Features for Identification of Propaganda Spans in News Articles

机译:结合BERT与上下文语言特征,用于识别新闻文章中的宣传跨度

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Recent endeavours at detection of propaganda in news articles treat this as a fine-grained problem of detecting it within fragments; and hence, transformer based embeddings perform decently in such detection. We build our propaganda detection framework on top of a transformer model simultaneously enriching it with contextual linguistic information of surrounding part-of-speech tags and LIWC categories the word itself belongs to. The evaluation outcomes being encouraging indicate a huge potential for this line of reasoning in natural language processing of news text.
机译:在新闻文章中检测宣传的最新努力将其视为检测碎片内的细粒度问题;因此,基于变压器的嵌入物在这种检测中变得非常效果。我们在变压器模型的顶部构建我们的宣传检测框架,同时通过围绕语音部分的语境语言信息来丰富它,围绕语音标签和LIWC类别这个词本身属于。令人鼓舞的评估结果表明这一推理在新闻文本的自然语言处理中的巨大潜力。

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