iLab-Edinburgh at SemEval-2016 Task 7: A Hybrid Approach for Determining Sentiment Intensity of Arabic Twitter Phrases

Edinburgh at SemEval-2022 Task 1: Jointly Fishing for Word Embeddings and Definitions

This paper presents a winning submission to the SemEval 2022 Task 1 on two sub-tasks: reverse dictionary and definition modelling. We leverage a recently proposed unified model with multi-task training. It utilizes data …

GWU NLP at SemEval-2019 Task 7: Hybrid Pipeline for Rumour Veracity and Stance Classification on Social Media

Social media plays a crucial role as the main resource news for information seekers online. However, the unmoderated feature of social media platforms lead to the emergence and spread of untrustworthy contents which harm…

DFKI-DKT at SemEval-2017 Task 8: Rumour Detection and Classification using Cascading Heuristics

We describe our submissions for SemEval-2017 Task 8, Determining Rumour Veracity and Support for Rumours. The Digital Curation Technologies (DKT) team at the German Research Center for Artificial Intelligence (DFKI) part…

SRHR at SemEval-2017 Task 6: Word Associations for Humour Recognition

This paper explores the role of semantic relatedness features, such as word associations, in humour recognition. Specifically, we examine the task of inferring pairwise humour judgments in Twitter hashtag wars. We examin…

SemEval-2016 Task 7: Determining Sentiment Intensity of English and Arabic Phrases