论文标题
使用自然语言处理和复发性神经网络中新文章中错误信息的分类
Classification of Misinformation in New Articles using Natural Language Processing and a Recurrent Neural Network
论文作者
论文摘要
本文旨在通过长期记忆复发的神经网络解决新闻文章中错误信息的分类。文章从2018年发表；一年中充斥着记者，撰写了有关唐纳德·特朗普总统，特别顾问罗伯特·穆勒，国际足联世界杯和俄罗斯的一年。该模型成功地以0.779944的精度得分成功地对这些文章进行了分类。我们认为这是成功的，因为该模型经过了包括英语以外的其他语言以及不完整或分散文章的文章的培训。
This paper seeks to address the classification of misinformation in news articles using a Long Short Term Memory Recurrent Neural Network. Articles were taken from 2018; a year that was filled with reporters writing about President Donald Trump, Special Counsel Robert Mueller, the Fifa World Cup, and Russia. The model presented successfully classifies these articles with an accuracy score of 0.779944. We consider this to be successful because the model was trained on articles that included languages other than English as well as incomplete, or fragmented, articles.
