EUDETECTOR: Leveraging Language Model to Identify EU-Related News

Koustav Rudra, Danny Tran, M. Shaltev
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Abstract

News media reflects the present state of a country or region to its audiences. Media outlets of a region post different kinds of news for their local and global audiences. In this paper, we focus on Europe (precisely EU) and propose a method to identify news that has an impact on Europe from any aspect such as financial, business, crime, politics, etc. Predicting the location of the news is itself a challenging task. Most of the approaches restrict themselves towards named entities or handcrafted features. In this paper, we try to overcome that limitation i.e., instead of focusing only on the named entities (Europe location, politicians etc.) and some hand-crafted rules, we also explore the context of news articles with the help of pre-trained language model BERT. The auto-regressive language model based European news detector shows about 9-19% improvement in terms of F-score over baseline models. Interestingly, we observe that such models automatically capture named entities, their origin, etc; hence, no separate information is required. We also evaluate the role of such entities in the prediction and explore the tokens that BERT really looks at for deciding the news category. Entities such as person, location, organization turn out to be good rationale tokens for the prediction.
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欧盟检测器:利用语言模型识别欧盟相关新闻
新闻媒体向受众反映一个国家或地区的现状。一个地区的媒体为当地和全球的受众发布不同类型的新闻。在本文中,我们将重点放在欧洲(确切地说是欧盟),并提出一种方法来识别从金融,商业,犯罪,政治等任何方面对欧洲产生影响的新闻。预测新闻的位置本身就是一项具有挑战性的任务。大多数方法都局限于命名实体或手工制作的特性。在本文中,我们试图克服这一限制,即,我们不是只关注命名实体(欧洲位置,政治家等)和一些手工制作的规则,而是在预训练的语言模型BERT的帮助下探索新闻文章的上下文。基于自回归语言模型的欧洲新闻检测器在F-score方面比基线模型提高了9-19%。有趣的是,我们观察到这样的模型自动捕获命名实体,它们的起源等;因此,不需要单独的信息。我们还评估了这些实体在预测中的作用,并探索了BERT在决定新闻类别时真正考虑的令牌。人员、位置、组织等实体被证明是预测的良好理由标记。
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