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2021 EACL EACL 2021

Findings of the Shared Task on Offensive Language Identification in Tamil, Malayalam, and Kannada

Abstract

AbstractDetecting offensive language in social media in local languages is critical for moderating user-generated content. Thus, the field of offensive language identification in under-resourced Tamil, Malayalam and Kannada languages are essential. As the user-generated content is more code-mixed and not well studied for under-resourced languages, it is imperative to create resources and conduct benchmarking studies to encourage research in under-resourced Dravidian languages. We created a shared task on offensive language detection in Dravidian languages. We summarize here the dataset for this challenge which are openly available at https://competitions.codalab.org/competitions/27654, and present an overview of the methods and the results of the competing systems.

🌉 Interdisciplinary Bridge — Machine Learning and Natural Language Processing
🐣 Hot Topic Early Bird — code-mixed language
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Bharathi Raja Chakravarthi , Ruba Priyadharshini , Navya Jose , Anand Kumar M , Thomas Mandl , Prasanna Kumar Kumaresan , Rahul Ponnusamy , Hariharan R L , John Philip McCrae , Elizabeth Sherly

Topics

Machine Learning > Core Methods > Classification Natural Language Processing > Applications > Sentiment Analysis

Keywords

text classification offensive language detection machine learning social media code-mixed language under-resourced language dravidian language
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