@inproceedings{pedersen-2019-duluth,
title = "{D}uluth at {S}em{E}val-2019 Task 6: Lexical Approaches to Identify and Categorize Offensive Tweets",
author = "Pedersen, Ted",
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/S19-2106",
doi = "10.18653/v1/S19-2106",
pages = "593--599",
abstract = "This paper describes the Duluth systems that participated in SemEval{--}2019 Task 6, Identifying and Categorizing Offensive Language in Social Media (OffensEval). For the most part these systems took traditional Machine Learning approaches that built classifiers from lexical features found in manually labeled training data. However, our most successful system for classifying a tweet as offensive (or not) was a rule-based black{--}list approach, and we also experimented with combining the training data from two different but related SemEval tasks. Our best systems in each of the three OffensEval tasks placed in the middle of the comparative evaluation, ranking 57th of 103 in task A, 39th of 75 in task B, and 44th of 65 in task C.",
}
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%0 Conference Proceedings
%T Duluth at SemEval-2019 Task 6: Lexical Approaches to Identify and Categorize Offensive Tweets
%A Pedersen, Ted
%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 pedersen-2019-duluth
%X This paper describes the Duluth systems that participated in SemEval–2019 Task 6, Identifying and Categorizing Offensive Language in Social Media (OffensEval). For the most part these systems took traditional Machine Learning approaches that built classifiers from lexical features found in manually labeled training data. However, our most successful system for classifying a tweet as offensive (or not) was a rule-based black–list approach, and we also experimented with combining the training data from two different but related SemEval tasks. Our best systems in each of the three OffensEval tasks placed in the middle of the comparative evaluation, ranking 57th of 103 in task A, 39th of 75 in task B, and 44th of 65 in task C.
%R 10.18653/v1/S19-2106
%U https://aclanthology.org/S19-2106
%U https://doi.org/10.18653/v1/S19-2106
%P 593-599
Markdown (Informal)
[Duluth at SemEval-2019 Task 6: Lexical Approaches to Identify and Categorize Offensive Tweets](https://aclanthology.org/S19-2106) (Pedersen, SemEval 2019)
ACL