@inproceedings{rohanian-etal-2018-wlv,
title = "{WLV} at {S}em{E}val-2018 Task 3: Dissecting Tweets in Search of Irony",
author = "Rohanian, Omid and
Taslimipoor, Shiva and
Evans, Richard and
Mitkov, Ruslan",
editor = "Apidianaki, Marianna and
Mohammad, Saif M. and
May, Jonathan and
Shutova, Ekaterina and
Bethard, Steven and
Carpuat, Marine",
booktitle = "Proceedings of the 12th International Workshop on Semantic Evaluation",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S18-1090",
doi = "10.18653/v1/S18-1090",
pages = "553--559",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T WLV at SemEval-2018 Task 3: Dissecting Tweets in Search of Irony
%A Rohanian, Omid
%A Taslimipoor, Shiva
%A Evans, Richard
%A Mitkov, Ruslan
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Bethard, Steven
%Y Carpuat, Marine
%S Proceedings of the 12th International Workshop on Semantic Evaluation
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F rohanian-etal-2018-wlv
%X 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.
%R 10.18653/v1/S18-1090
%U https://aclanthology.org/S18-1090
%U https://doi.org/10.18653/v1/S18-1090
%P 553-559
Markdown (Informal)
[WLV at SemEval-2018 Task 3: Dissecting Tweets in Search of Irony](https://aclanthology.org/S18-1090) (Rohanian et al., SemEval 2018)
ACL