Darshini Mahendran


NLP@VCU: Identifying Adverse Effects in English Tweets for Unbalanced Data
Darshini Mahendran | Cora Lewis | Bridget McInnes
Proceedings of the Fifth Social Media Mining for Health Applications Workshop & Shared Task

This paper describes our participation in the Social Media Mining for Health Application (SMM4H 2020) Challenge Track 2 for identifying tweets containing Adverse Effects (AEs). Our system uses Convolutional Neural Networks. We explore downsampling, oversampling, and adjusting the class weights to account for the imbalanced nature of the dataset. Our results showed downsampling outperformed oversampling and adjusting the class weights on the test set however all three obtained similar results on the development set.


SciREL at SemEval-2018 Task 7: A System for Semantic Relation Extraction and Classification
Darshini Mahendran | Chathurika Brahmana | Bridget McInnes
Proceedings of the 12th International Workshop on Semantic Evaluation

This paper describes our system, SciREL (Scientific abstract RELation extraction system), developed for the SemEval 2018 Task 7: Semantic Relation Extraction and Classification in Scientific Papers. We present a feature-vector based system to extract explicit semantic relation and classify them. Our system is trained in the ACL corpus (BIrd et al., 2008) that contains annotated abstracts given by the task organizers. When an abstract with annotated entities is given as the input into our system, it extracts the semantic relations through a set of defined features and classifies them into one of the given six categories of relations through feature engineering and a learned model. For the best combination of features, our system SciREL obtained an F-measure of 20.03 on the official test corpus which includes 150 abstracts in the relation classification Subtask 1.1. In this paper, we provide an in-depth error analysis of our results to prevent duplication of research efforts in the development of future systems