I am a 2nd year Ph.D. student in the Department of Computer Science at The Ohio State University. My research interests are natural language processing, machine learning and social media . I am currently working in the field of text simplification and stylistics with Professor Wei Xu as my advisor.
I received my Master's degree in Computer and Information Science from University of Pennsylvania (UPENN) and my Bachelor's degree in Computer Science and Engineering from International Institute of Information Technology Hyderabad (IIIT). At UPENN, I worked in the field of social media text analytics and at IIIT, I worked in data visualization. Prior to joining OSU, I also worked as a Software Development Engineer II at Big Data Technologies, Amazon .
If you want to contact me, please drop me an email at email@example.com
Current lexical simplification approaches rely heavily on heuristics and corpus level features that do not always align with human judgment. We create a human-rated word-complexity lexicon of 15,000 English words and propose a novel neural readability ranking model with a Gaussian-based feature vectorization layer that utilizes these human ratings to measure the complexity of any given word or phrase. Our model performs better than the state-of-the-art systems for different lexical simplification tasks and evaluation datasets. Additionally, we also produce SimplePPDB++, a lexical resource of over 10 million simplifying paraphrase rules, by applying our model to the Paraphrase Database (PPDB).
The project was a part of World Well Being Project (WWBP). We captured the different sources and interpretations of well-being across various cultures or countries. using the context of sentiment words and their distribution across countries.
We developed a new visualization system called CROVHD (Concentric Rings of Visualization of High Dimensional Data) to visualize the high dimensional data as a 2-D representation. We also extended this system to 3-D Cone visualization to visualize k-nearest neighbours.