SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)

@inproceedings{Zampieri2019SemEval2019T6,
  title={SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)},
  author={Marcos Zampieri and Shervin Malmasi and Preslav Nakov and Sara Rosenthal and Noura Farra and Ritesh Kumar},
  booktitle={*SEMEVAL},
  year={2019}
}
We present the results and the main findings of SemEval-2019 Task 6 on Identifying and Categorizing Offensive Language in Social Media (OffensEval. [...] Key Result In total, about 800 teams signed up to participate in the task, and 115 of them submitted results, which we present and analyze in this report.Expand

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Predicting the Type and Target of Offensive Posts in Social Media
TLDR
The Offensive Language Identification Dataset (OLID), a new dataset with tweets annotated for offensive content using a fine-grained three-layer annotation scheme, is complied and made publicly available.
LaSTUS/TALN at SemEval-2019 Task 6: Identification and Categorization of Offensive Language in Social Media with Attention-based Bi-LSTM model
TLDR
A bidirectional Long-Short Term Memory network for identifying offensive language in Twitter using a pre-trained Word Embeddings in tweet data, including information about emojis and hashtags achieves good performance in the three sub-tasks.
The Titans at SemEval-2019 Task 6: Offensive Language Identification, Categorization and Target Identification
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This system paper is a description of the system submitted to “SemEval-2019 Task 6”, where the system had to detect offensive language in Twitter and classify the targeted audience.
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References

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MIDAS at SemEval-2019 Task 6: Identifying Offensive Posts and Targeted Offense from Twitter
TLDR
This paper presents the approach and the system description for Sub Task A and Sub Task B of SemEval 2019 Task 6: Identifying and Categorizing Offensive Language in Social Media, and provides detailed analysis of the results obtained using the trained models.
Duluth at SemEval-2019 Task 6: Lexical Approaches to Identify and Categorize Offensive Tweets
TLDR
This paper describes the Duluth systems that participated in SemEval–2019 Task 6, Identifying and Categorizing Offensive Language in Social Media (OffensEval), and finds that the most successful system for classifying a tweet as offensive (or not) was a rule-based black–list approach.
HAD-Tübingen at SemEval-2019 Task 6: Deep Learning Analysis of Offensive Language on Twitter: Identification and Categorization
TLDR
The submissions of the HAD-Tübingen team for the SemEval 2019 - Task 6: “OffensEval: Identifying and Categorizing Offensive Language in Social Media”, using a Long short-term memory recurrent neural network (LSTM) to identify and categorize offensive tweets.
bhanodaig at SemEval-2019 Task 6: Categorizing Offensive Language in social media
TLDR
The work that the team bhanodaig did at Indian Institute of Technology towards OffensEval i.e. identifying and categorizing offensive language in social media is described, with encouraging enough to work for better results in future.
Pardeep at SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media using Deep Learning
TLDR
The proposed approach solves 3 different Sub-tasks provided in the SemEval-2019 task 6 which incorporates identification of offensive tweets as well as their categorization, validating the fact that the proposed models can be used for automating the offensive post-detection task in social media.
Predicting the Type and Target of Offensive Posts in Social Media
TLDR
The Offensive Language Identification Dataset (OLID), a new dataset with tweets annotated for offensive content using a fine-grained three-layer annotation scheme, is complied and made publicly available.
LaSTUS/TALN at SemEval-2019 Task 6: Identification and Categorization of Offensive Language in Social Media with Attention-based Bi-LSTM model
TLDR
A bidirectional Long-Short Term Memory network for identifying offensive language in Twitter using a pre-trained Word Embeddings in tweet data, including information about emojis and hashtags achieves good performance in the three sub-tasks.
TECHSSN at SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Tweets using Deep Neural Networks
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
KMI-Coling at SemEval-2019 Task 6: Exploring N-grams for Offensive Language detection
TLDR
This paper presents the system description of Offensive language detection tool which is developed by the KMI_Coling under the OffensEval Shared Task was conducted in SemEval 2019 workshop.
Fermi at SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media using Sentence Embeddings
TLDR
This paper describes the Fermi system (Fermi) for Task 6: OffensEval: Identifying and Categorizing Offensive Language in Social Media of SemEval-2019 and provides a detailed description of the approach, as well as the results obtained for the task.
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