Research output per year
Research output per year
Abstract
Abusive language detection is an unsolved and challenging problem for the NLP community. Recent literature suggests various
approaches to distinguish between different language phenomena (e.g., hate speech vs. cyberbullying vs. offensive language) and factors
(degree of explicitness and target) that may help to classify different abusive language phenomena. There are data sets that annotate the
target of abusive messages (i.e.OLID/OffensEval (Zampieri et al., 2019a)). However, there is a lack of data sets that take into account the
degree of explicitness. In this paper, we propose annotation guidelines to distinguish between explicit and implicit abuse in English and
apply them to OLID/OffensEval. The outcome is a newly created resource, AbuseEval v1.0, which aims to address some of the existing
issues in the annotation of offensive and abusive language (e.g., explicitness of the message, presence of a target, need of context, and
interaction across different phenomena)
approaches to distinguish between different language phenomena (e.g., hate speech vs. cyberbullying vs. offensive language) and factors
(degree of explicitness and target) that may help to classify different abusive language phenomena. There are data sets that annotate the
target of abusive messages (i.e.OLID/OffensEval (Zampieri et al., 2019a)). However, there is a lack of data sets that take into account the
degree of explicitness. In this paper, we propose annotation guidelines to distinguish between explicit and implicit abuse in English and
apply them to OLID/OffensEval. The outcome is a newly created resource, AbuseEval v1.0, which aims to address some of the existing
issues in the annotation of offensive and abusive language (e.g., explicitness of the message, presence of a target, need of context, and
interaction across different phenomena)
| Original language | English |
|---|---|
| Pages | 1-11 |
| Number of pages | 12 |
| Publication status | Published - 2020 |
| Event | 12th Language Resources and Evaluation Conference : LREC 2020 - Marseille, France Duration: 11-May-2020 → 16-May-2020 https://lrec2020.lrec-conf.org/en/ |
Conference
| Conference | 12th Language Resources and Evaluation Conference |
|---|---|
| Country/Territory | France |
| City | Marseille |
| Period | 11/05/2020 → 16/05/2020 |
| Internet address |
Fingerprint
Dive into the research topics of 'I Feel Offended, Don’t Be Abusive! Implicit/Explicit Messages in Offensive and Abusive Language'. Together they form a unique fingerprint.Research output
- 105 Citations
- 1 Conference contribution
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GruPaTo at SemEval-2020 Task 12: Retraining mBERT on Social Media and Fine-tuned Offensive Language Models
Caselli, T., Colla, D., Basile, V., Mitrović, J. & Granitzer, M., 2020, Proceedings of the 14th International Workshop on Semantic Evaluation. Herbelot, A., Zhu, X., Palmer, A., Schneider, N., May, J. & Shutova, E. (eds.). Association for Computational Linguistics (ACL), 9 p.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
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