Deparment: Computer Science, Mathematics
Mentor: Dr. Kumer Pial Das
Title: "Semantic Similarity of Documents Using Latent Semantic Analysis"
Latent Semantic Analysis (LSA) is a technique that analyzes relationships between documents and its terms and discovers a data representation that has a lower dimension than the original semantic space. Essentially, the reduced dimensionality preserves the mosts crucial aspects of the data since LSA analyzes documents to find latent meaning. The semantic space is determined by singular value decomposition (SVD), which decomposes any given matrix into a product of factorized components. LSA will be further studied to understand how semantic models can be used to improve the search of a particular interest of a domain, such as newspaper articles and literature.