@inproceedings{rusert-srinivasan-2019-nlp,
title = "{NLP}@{UIOWA} at {S}em{E}val-2019 Task 6: Classifying the Crass using Multi-windowed {CNN}s",
author = "Rusert, Jonathan and
Srinivasan, Padmini",
editor = "May, Jonathan and
Shutova, Ekaterina and
Herbelot, Aurelie and
Zhu, Xiaodan and
Apidianaki, Marianna and
Mohammad, Saif M.",
booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S19-2125/",
doi = "10.18653/v1/S19-2125",
pages = "704--711",
abstract = "This paper proposes a system for OffensEval (SemEval 2019 Task 6), which calls for a system to classify offensive language into several categories. Our system is a text based CNN, which learns only from the provided training data. Our system achieves 80 - 90{\%} accuracy for the binary classification problems (offensive vs not offensive and targeted vs untargeted) and 63{\%} accuracy for trinary classification (group vs individual vs other)."
}
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<abstract>This paper proposes a system for OffensEval (SemEval 2019 Task 6), which calls for a system to classify offensive language into several categories. Our system is a text based CNN, which learns only from the provided training data. Our system achieves 80 - 90% accuracy for the binary classification problems (offensive vs not offensive and targeted vs untargeted) and 63% accuracy for trinary classification (group vs individual vs other).</abstract>
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%0 Conference Proceedings
%T NLP@UIOWA at SemEval-2019 Task 6: Classifying the Crass using Multi-windowed CNNs
%A Rusert, Jonathan
%A Srinivasan, Padmini
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%S Proceedings of the 13th International Workshop on Semantic Evaluation
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota, USA
%F rusert-srinivasan-2019-nlp
%X This paper proposes a system for OffensEval (SemEval 2019 Task 6), which calls for a system to classify offensive language into several categories. Our system is a text based CNN, which learns only from the provided training data. Our system achieves 80 - 90% accuracy for the binary classification problems (offensive vs not offensive and targeted vs untargeted) and 63% accuracy for trinary classification (group vs individual vs other).
%R 10.18653/v1/S19-2125
%U https://aclanthology.org/S19-2125/
%U https://doi.org/10.18653/v1/S19-2125
%P 704-711
Markdown (Informal)
[NLP@UIOWA at SemEval-2019 Task 6: Classifying the Crass using Multi-windowed CNNs](https://aclanthology.org/S19-2125/) (Rusert & Srinivasan, SemEval 2019)
ACL