Latent Semantic Analysis

Latent Semantic Analysis (LSA) is a dimensionality reduction technique used to uncover the underlying semantic structure in text data by representing documents and words as vectors in a lower-dimensional space. Current research focuses on improving LSA's performance through integration with other methods, such as non-negative matrix factorization, and its application in conjunction with deep learning models like BERT and advanced language models for tasks like document retrieval, text summarization, and topic modeling. This work highlights LSA's continued relevance in natural language processing, particularly for enhancing the accuracy and efficiency of information retrieval and analysis across diverse applications, including legal text analysis and ESG impact assessment.