@inproceedings{kumar-etal-2019-bhanodaig,
title = "bhanodaig at {S}em{E}val-2019 Task 6: Categorizing Offensive Language in social media",
author = "Kumar, Ritesh and
Bhanodai, Guggilla and
Pamula, Rajendra and
Chennuru, Maheswara Reddy",
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/S19-2098/",
doi = "10.18653/v1/S19-2098",
pages = "547--550",
abstract = "This paper describes the work that our team bhanodaig did at Indian Institute of Technology (ISM) towards OffensEval i.e. identifying and categorizing offensive language in social media. Out of three sub-tasks, we have participated in sub-task B: automatic categorization of offensive types. We perform the task of categorizing offensive language, whether the tweet is targeted insult or untargeted. We use Linear Support Vector Machine for classification. The official ranking metric is macro-averaged F1. Our system gets the score 0.5282 with accuracy 0.8792. However, as new entrant to the field, our scores are encouraging enough to work for better results in future."
}
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<abstract>This paper describes the work that our team bhanodaig did at Indian Institute of Technology (ISM) towards OffensEval i.e. identifying and categorizing offensive language in social media. Out of three sub-tasks, we have participated in sub-task B: automatic categorization of offensive types. We perform the task of categorizing offensive language, whether the tweet is targeted insult or untargeted. We use Linear Support Vector Machine for classification. The official ranking metric is macro-averaged F1. Our system gets the score 0.5282 with accuracy 0.8792. However, as new entrant to the field, our scores are encouraging enough to work for better results in future.</abstract>
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%0 Conference Proceedings
%T bhanodaig at SemEval-2019 Task 6: Categorizing Offensive Language in social media
%A Kumar, Ritesh
%A Bhanodai, Guggilla
%A Pamula, Rajendra
%A Chennuru, Maheswara Reddy
%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 kumar-etal-2019-bhanodaig
%X This paper describes the work that our team bhanodaig did at Indian Institute of Technology (ISM) towards OffensEval i.e. identifying and categorizing offensive language in social media. Out of three sub-tasks, we have participated in sub-task B: automatic categorization of offensive types. We perform the task of categorizing offensive language, whether the tweet is targeted insult or untargeted. We use Linear Support Vector Machine for classification. The official ranking metric is macro-averaged F1. Our system gets the score 0.5282 with accuracy 0.8792. However, as new entrant to the field, our scores are encouraging enough to work for better results in future.
%R 10.18653/v1/S19-2098
%U https://aclanthology.org/S19-2098/
%U https://doi.org/10.18653/v1/S19-2098
%P 547-550
Markdown (Informal)
[bhanodaig at SemEval-2019 Task 6: Categorizing Offensive Language in social media](https://aclanthology.org/S19-2098/) (Kumar et al., SemEval 2019)
ACL