Ratnasingam Sakuntharaj


2024

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Overview of Second Shared Task on Sentiment Analysis in Code-mixed Tamil and Tulu
Lavanya Sambath Kumar | Asha Hegde | Bharathi Raja Chakravarthi | Hosahalli Shashirekha | Rajeswari Natarajan | Sajeetha Thavareesan | Ratnasingam Sakuntharaj | Thenmozhi Durairaj | Prasanna Kumar Kumaresan | Charmathi Rajkumar
Proceedings of the Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages

Sentiment Analysis (SA) in Dravidian codemixed text is a hot research area right now. In this regard, the “Second Shared Task on SA in Code-mixed Tamil and Tulu” at Dravidian- LangTech (EACL-2024) is organized. Two tasks namely SA in Tamil-English and Tulu- English code-mixed data, make up this shared assignment. In total, 64 teams registered for the shared task, out of which 19 and 17 systems were received for Tamil and Tulu, respectively. The performance of the systems submitted by the participants was evaluated based on the macro F1-score. The best method obtained macro F1-scores of 0.260 and 0.584 for code-mixed Tamil and Tulu texts, respectively.

2021

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IIITT at CASE 2021 Task 1: Leveraging Pretrained Language Models for Multilingual Protest Detection
Pawan Kalyan | Duddukunta Reddy | Adeep Hande | Ruba Priyadharshini | Ratnasingam Sakuntharaj | Bharathi Raja Chakravarthi
Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021)

In a world abounding in constant protests resulting from events like a global pandemic, climate change, religious or political conflicts, there has always been a need to detect events/protests before getting amplified by news media or social media. This paper demonstrates our work on the sentence classification subtask of multilingual protest detection in CASE@ACL-IJCNLP 2021. We approached this task by employing various multilingual pre-trained transformer models to classify if any sentence contains information about an event that has transpired or not. We performed soft voting over the models, achieving the best results among the models, accomplishing a macro F1-Score of 0.8291, 0.7578, and 0.7951 in English, Spanish, and Portuguese, respectively.