Hariharan R L


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 https://competitions.codalab.org/competitions/27654, and present an overview of the methods and the results of the competing systems.


NITK NLP at FinCausal-2020 Task 1 Using BERT and Linear models.
Hariharan R L | Anand Kumar M
Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation

FinCausal-2020 is the shared task which focuses on the causality detection of factual data for financial analysis. The financial data facts don’t provide much explanation on the variability of these data. This paper aims to propose an efficient method to classify the data into one which is having any financial cause or not. Many models were used to classify the data, out of which SVM model gave an F-Score of 0.9435, BERT with specific fine-tuning achieved best results with F-Score of 0.9677.