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A Time-based Collective Factorization for Topic Discovery and Monitoring in News

机译：基于时间的集体因式分解，用于新闻中的主题发现和监视

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Discovering and tracking topic shifts in news constitutes a new challenge for applications nowadays. Topics evolve, emerge and fade, making it more difficult for the journalist - or the press consumer - to decrypt the news. For instance, the current Syrian chemical crisis has been the starting point of the UN Russian initiative and also the revival of the US France alliance. A topical mapping representing how the topics evolve in time would be helpful to contex-tualize information. As far as we know, few topic tracking systems can provide such temporal topic connections. In this paper, we introduce a novel framework inspired from Collective Factorization for online topic discovery able to connect topics between different time-slots. The framework learns jointly the topics evolution and their time dependencies. It offers the user the ability to control, through one unique hyper-parameter, the tradeoff between the past accumulated knowledge and the current observed data. We show, on semi-synthetic datasets and on Yahoo News articles, that our method is competitive with state-of-the-art techniques while providing a simple way to monitor topics evolution (including emerging and disappearing topics).
机译：发现和跟踪新闻中的主题变化对当今的应用程序构成了新的挑战。话题不断演变，浮现和消退，这使新闻记者（或新闻消费者）解密新闻变得更加困难。例如，当前的叙利亚化学危机一直是联合国俄罗斯倡议的起点，也是美法同盟的复兴。表示主题如何随时间演变的主题映射将有助于信息的概念化。据我们所知，很少有主题跟踪系统可以提供这样的时间主题连接。在本文中，我们介绍了一个基于集体分解的新颖框架，用于在线主题发现，该主题能够将不同时隙之间的主题联系起来。该框架共同学习主题演变及其时间依赖性。它使用户能够通过一个唯一的超参数来控制过去累积的知识与当前观察到的数据之间的折衷。在半合成数据集和Yahoo News文章上，我们证明了我们的方法与最先进的技术相比具有竞争优势，同时提供了一种监视主题演变（包括新兴主题和消失主题）的简单方法。

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