Understanding Satirical Articles Using Common-Sense
Automatic satire detection is a subtle text classification task, for machines and at times, even for humans. In this paper we argue that satire detection should be approached using common-sense inferences, rather than traditional text classification methods. We present a highly structured latent variable model capturing the required inferences. The model abstracts over the specific entities appearing in the articles, grouping them into generalized categories, thus allowing the model to adapt to previously unseen situations.
Code (0)
등록된 구현이 없습니다.
Tasks
ArticlesClassificationCommon Sense ReasoningGeneral ClassificationSatire Detectiontext-classificationText ClassificationSimilar Papers 제목 키워드 기반
SatiricLR: a Language Resource of Satirical News Articles
In this paper we introduce the Satirical Language Resource: a dataset containing a balanced collection of satirical and non satirical news texts from various domains. This is the first dataset of this magnitude and scope…
ArticlesGeneral ClassificationSatirical News Detection with Semantic Feature Extraction and Game-theoretic Rough Sets
Satirical news detection is an important yet challenging task to prevent spread of misinformation. Many feature based and end-to-end neural nets based satirical news detection systems have been proposed and delivered pro…
ArticlesMisinformationMINT -- Mainstream and Independent News Text Corpus
Most corpora approach misinformation as a binary problem, classifying texts as real or fake. However, they fail to consider the diversity of existing textual genres and types, which present different properties usually a…
ArticlesDiversityMisinformationContext-Driven Satirical News Generation
While mysterious, humor likely hinges on an interplay of entities, their relationships, and cultural connotations. Motivated by the importance of context in humor, we consider methods for constructing and leveraging cont…
ArticlesNews GenerationYesBut: A High-Quality Annotated Multimodal Dataset for evaluating Satire Comprehension capability of Vision-Language Models
Understanding satire and humor is a challenging task for even current Vision-Language models. In this paper, we propose the challenging tasks of Satirical Image Detection (detecting whether an image is satirical), Unders…
BenchmarkingImage Captioning