SemEval-2018 Task 3: Irony Detection in English Tweets

SEMEVAL 2018 Cynthia Van HeeEls LefeverV{\'e}ronique Hoste

This paper presents the first shared task on irony detection: given a tweet, automatic natural language processing systems should determine whether the tweet is ironic (Task A) and which type of irony (if any) is expressed (Task B). The ironic tweets were collected using irony-related hashtags (i.e. {\#}irony, {\#}sarcasm, {\#}not) and were subsequently manually annotated to minimise the amount of noise in the corpus... (read more)

PDF Abstract


No code implementations yet. Submit your code now

Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper

🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet