Mowjaz多主题标签任务的MohammadHabash团队

Mohammad Habash
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引用次数: 1

摘要

随着数据规模的不断增长,多标签文本分类是一个重要的问题,而且由于互联网用户倾向于为每个文本样本分配多个标签来描述文档、电子邮件、帖子等,因此难以为每个文本样本分配单个标签。我们的目标是预测给定文本的文章的类别(主题)。在这项工作中使用的数据集包含来自Mowjaz的文章。Mowjaz是一个阿拉伯语主题内容聚合移动应用程序,用户可以关注来自顶级出版商的新闻,体育,娱乐和其他主题。本文描述了使用带有AraVec嵌入的双向门控循环单元(Bi-GRU)对文章进行分类的方法。该系统的f1评分为0.8344,较基线模型有显著改善。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Team MohammadHabash at Mowjaz Multi-Topic Labelling Task
Multi-label text classification is an important problem with the growing size of data and the difficulties in assigning a single label to each text sample because of the tendency of internet users to assign multiple labels to describe documents, emails, posts, etc. Our goal is to predict the category (topic) of an article given its text. The dataset which is used in this work contains articles from Mowjaz. Mowjaz is an Arabic topical content aggregation mobile application for news, sport, entertainment and other topics from top publishers that users can follow. This paper describes the approach to classify articles using Bi-directional Gated Recurrent Unit (Bi-GRU) with AraVec embeddings. The F1-score of this system is 0.8344 which shows a significant improvement over the baseline models.
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