Deep learning Approach for Question and Answering

Introduction Question answering is one of the most challenging applications we have in Natural Language Processing. It is widely used in applications like information retrieval and entity extraction. It is also used as the back-end framework for systems like chatbots and for simulating human-like conversations. While it is ambitious to create and evaluate the performance of an agent in general dialogue, it is relatively easier to evaluate its responses to input questions.

November 10, 2020 Read
Facebook Political Ad analysis

This project explores Facebook ads run by the Presidential candidates and Congressional candidates in recent US elections. Using Natural Language Processing techniques we analyze the content, tone, and targets of digital communications in an effort to better understand the kinds of messages that candidates highlight to voters online. Project Vision Observers have speculated that political ads on social media contribute to growing divisions in American society, engaging in partisan mudslinging and uncivil attacks over respectful discussion of the issues.

August 1, 2020 Read
Matrix Factorization methods for Recommendor Systems

This project compares two deep learning methods used in recommendation systems and compares the results of these methods on MovieLens dataset. Abstract Recommender systems are widely used tools to make personalized recommendations to individuals. Neighborhood and factor models have been extremely popular techniques in the field of recommender systems. However, given the successful application of neural networkbased techniques in other fields of technology such as computer vision and natural language processing, it is reasonable to believe that neural network-based techniques would also perform well in recommender systems.

March 1, 2020 Read