115 citations · 194 across the 7 of their papers we have counts for
7 papers
Sarang at DEFACTIFY 4.0: Detecting AI-Generated Text Using Noised Data and an Ensemble of DeBERTa Models
Avinash Trivedi, Sangeetha Sivanesan
This paper presents an effective approach to detect AI-generated text, developed for the Defactify 4.0 shared task at the fourth workshop on multimodal fact checking and hate speec…
Findings of the Shared Task on Offensive Span Identification from Code-Mixed Tamil-English Comments
Manikandan Ravikiran, Bharathi Raja Chakravarthi, Anand Kumar Madasamy +5
Offensive content moderation is vital in social media platforms to support healthy online discussions. However, their prevalence in codemixed Dravidian languages is limited to clas…
AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language Processing
Katikapalli Subramanyam Kalyan, Ajit Rajasekharan, Sivanesan Sangeetha
Transformer-based pretrained language models (T-PTLMs) have achieved great success in almost every NLP task. The evolution of these models started with GPT and BERT. These models a…
AMMU : A Survey of Transformer-based Biomedical Pretrained Language Models
Katikapalli Subramanyam Kalyan, Ajit Rajasekharan, Sivanesan Sangeetha
Transformer-based pretrained language models (PLMs) have started a new era in modern natural language processing (NLP). These models combine the power of transformers, transfer lea…
Want to Identify, Extract and Normalize Adverse Drug Reactions in Tweets? Use RoBERTa
Katikapalli Subramanyam Kalyan, S. Sangeetha
This paper presents our approach for task 2 and task 3 of Social Media Mining for Health (SMM4H) 2020 shared tasks. In task 2, we have to differentiate adverse drug reaction (ADR)…
Medical Concept Normalization in User Generated Texts by Learning Target Concept Embeddings
Katikapalli Subramanyam Kalyan, S. Sangeetha
Medical concept normalization helps in discovering standard concepts in free-form text i.e., maps health-related mentions to standard concepts in a vocabulary. It is much beyond si…