论文标题
新闻头条数据集用于讽刺检测
News Headlines Dataset For Sarcasm Detection
论文作者
论文摘要
过去在讽刺检测中的研究主要利用使用基于主题标签的监督收集的Twitter数据集，但在标签和语言方面，此类数据集嘈杂。此外，许多推文都是对其他推文的答复，并且在这些推文中检测讽刺需要上下文推文的可用性。为了克服与Twitter数据集中的噪声相关的限制，我们从两个新闻网站中策划了新闻头条数据集：Theonion旨在生产时事的讽刺版本，而HuffPost发布了真实的新闻。该数据集包含大约28K头条新闻，其中13K是讽刺的。为了使其更有用，我们包括了新闻文章的源链接，以便可以根据需要提取更多数据。在本文中，我们描述了有关数据集和潜在用例的各种细节，除了讽刺检测。
Past studies in Sarcasm Detection mostly make use of Twitter datasets collected using hashtag-based supervision but such datasets are noisy in terms of labels and language. Furthermore, many tweets are replies to other tweets, and detecting sarcasm in these requires the availability of contextual tweets. To overcome the limitations related to noise in Twitter datasets, we curate News Headlines Dataset from two news websites: TheOnion aims at producing sarcastic versions of current events, whereas HuffPost publishes real news. The dataset contains about 28K headlines out of which 13K are sarcastic. To make it more useful, we have included the source links of the news articles so that more data can be extracted as needed. In this paper, we describe various details about the dataset and potential use cases apart from Sarcasm Detection.
