跨语言推文中主题和标签相关性的联合估计

Procheta Sen, Debasis Ganguly, G. Jones
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引用次数: 0

摘要

Twitter是一个被广泛使用的分享新闻文章的平台。在多语言社区中,一个新兴趋势是使用英语推特分享非英语新闻文章,以便将新闻传播给更广泛的受众。一般来说,此类tweet的相关标签的选择取决于非英语新闻文章的主题。在本文中,我们解决了自动检测此类推文的标签相关性的问题。更具体地说,我们提出了一个生成模型,共同对英语推文中的主题和其中共享的非英语新闻文章中的主题进行建模,以预测推文标签的相关性。为了进行实验,我们收集了一些用孟加拉语(一种南亚语言)分享新闻文章的英文推文。我们在该数据集上的实验表明,使用非英语新闻文章和推文的主题进行联合估计的方法比仅使用推文本身的主题进行相关性估计更有效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Joint Estimation of Topics and Hashtag Relevance in Cross-Lingual Tweets
Twitter is a widely used platform for sharing news articles. An emerging trend in multi-lingual communities is to share non-English news articles using English tweets in order to spread the news to a wider audience. In general, the choice of relevant hashtags for such tweets depends on the topic of the non-English news article. In this paper, we address the problem of automatically detecting the relevance of the hashtags of such tweets. More specifically, we propose a generative model to jointly model the topics within an English tweet and those within the non-English news article shared from it to predict the relevance of the hashtags of the tweet. For conducting experiments, we compiled a collection of English tweets that share news articles in Bengali (a South Asian language). Our experiments on this dataset demonstrate that this joint estimation based approach using the topics from both the non-English news articles and the tweets proves to be more effective for relevance estimation than that of only using the topics of a tweet itself.
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