Navya Jose


Findings of the Shared Task on Offensive Language Identification in Tamil, Malayalam, and Kannada
Bharathi Raja Chakravarthi | Ruba Priyadharshini | Navya Jose | Anand Kumar M | Thomas Mandl | Prasanna Kumar Kumaresan | Rahul Ponnusamy | Hariharan R L | John P. McCrae | Elizabeth Sherly
Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages

Detecting 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, and present an overview of the methods and the results of the competing systems.


A Sentiment Analysis Dataset for Code-Mixed Malayalam-English
Bharathi Raja Chakravarthi | Navya Jose | Shardul Suryawanshi | Elizabeth Sherly | John Philip McCrae
Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)

There is an increasing demand for sentiment analysis of text from social media which are mostly code-mixed. Systems trained on monolingual data fail for code-mixed data due to the complexity of mixing at different levels of the text. However, very few resources are available for code-mixed data to create models specific for this data. Although much research in multilingual and cross-lingual sentiment analysis has used semi-supervised or unsupervised methods, supervised methods still performs better. Only a few datasets for popular languages such as English-Spanish, English-Hindi, and English-Chinese are available. There are no resources available for Malayalam-English code-mixed data. This paper presents a new gold standard corpus for sentiment analysis of code-mixed text in Malayalam-English annotated by voluntary annotators. This gold standard corpus obtained a Krippendorff’s alpha above 0.8 for the dataset. We use this new corpus to provide the benchmark for sentiment analysis in Malayalam-English code-mixed texts.