@inproceedings{suseelan-etal-2019-techssn,
title = "{TECHSSN} at {S}em{E}val-2019 Task 6: Identifying and Categorizing Offensive Language in Tweets using Deep Neural Networks",
author = "Suseelan, Angel and
S, Rajalakshmi and
B, Logesh and
S, Harshini and
B, Geetika and
S, Dyaneswaran and
Rajendram, S Milton and
T T, Mirnalinee",
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/S19-2132",
doi = "10.18653/v1/S19-2132",
pages = "753--758",
abstract = "Task 6 of SemEval 2019 involves identifying and categorizing offensive language in social media. The systems developed by TECHSSN team uses multi-level classification techniques. We have developed two systems. In the first system, the first level of classification is done by a multi-branch 2D CNN classifier with Google{'}s pre-trained Word2Vec embedding and the second level of classification by string matching technique supported by offensive and bad words dictionary. The second system uses a multi-branch 1D CNN classifier with Glove pre-trained embedding layer for the first level of classification and string matching for the second level of classification. Input data with a probability of less than 0.70 in the first level are passed on to the second level. The misclassified examples are classified correctly in the second level.",
}
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<abstract>Task 6 of SemEval 2019 involves identifying and categorizing offensive language in social media. The systems developed by TECHSSN team uses multi-level classification techniques. We have developed two systems. In the first system, the first level of classification is done by a multi-branch 2D CNN classifier with Google’s pre-trained Word2Vec embedding and the second level of classification by string matching technique supported by offensive and bad words dictionary. The second system uses a multi-branch 1D CNN classifier with Glove pre-trained embedding layer for the first level of classification and string matching for the second level of classification. Input data with a probability of less than 0.70 in the first level are passed on to the second level. The misclassified examples are classified correctly in the second level.</abstract>
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%0 Conference Proceedings
%T TECHSSN at SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Tweets using Deep Neural Networks
%A Suseelan, Angel
%A S, Rajalakshmi
%A B, Logesh
%A S, Harshini
%A B, Geetika
%A S, Dyaneswaran
%A Rajendram, S. Milton
%A T T, Mirnalinee
%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 suseelan-etal-2019-techssn
%X Task 6 of SemEval 2019 involves identifying and categorizing offensive language in social media. The systems developed by TECHSSN team uses multi-level classification techniques. We have developed two systems. In the first system, the first level of classification is done by a multi-branch 2D CNN classifier with Google’s pre-trained Word2Vec embedding and the second level of classification by string matching technique supported by offensive and bad words dictionary. The second system uses a multi-branch 1D CNN classifier with Glove pre-trained embedding layer for the first level of classification and string matching for the second level of classification. Input data with a probability of less than 0.70 in the first level are passed on to the second level. The misclassified examples are classified correctly in the second level.
%R 10.18653/v1/S19-2132
%U https://aclanthology.org/S19-2132
%U https://doi.org/10.18653/v1/S19-2132
%P 753-758
Markdown (Informal)
[TECHSSN at SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Tweets using Deep Neural Networks](https://aclanthology.org/S19-2132) (Suseelan et al., SemEval 2019)
ACL
- Angel Suseelan, Rajalakshmi S, Logesh B, Harshini S, Geetika B, Dyaneswaran S, S Milton Rajendram, and Mirnalinee T T. 2019. TECHSSN at SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Tweets using Deep Neural Networks. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 753–758, Minneapolis, Minnesota, USA. Association for Computational Linguistics.