This paper describes the systems submitted to SemEval 2018 Task 3 "Irony detection in English tweets" for both subtasks A and B. The first system leveraging a combination of sentiment, distributional semantic, and text surface features is ranked third among 44 teams according to the official leaderboard of the subtask A. The second system with slightly different representation of the features ranked ninth in subtask B. We present a method that entails decomposing tweets into separate parts. Searching for contrast within the constituents of a tweet is an integral part of our system. We embrace an extensive definition of contrast which leads to a vast coverage in detecting ironic content.
In REF , researchers leverage a combination of sentiment, distributional semantic and text surface features.
WLV at SemEval-2018 Task 3: Dissecting Tweets in Search of Irony
SemEval@NAACL-HLT
Computer Science