Abstract
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.Type
Conference contributionLanguage
enDescription
International Workshop on Semantic Evaluation. WLV at SemEval-2018 Task 3.ISBN
9781948087209Sponsors
Research Group in Computational LinguisticsThe following licence applies to the copyright and re-use of this item:
- Creative Commons
Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by-nc-nd/4.0/