Exploring deeper on LSA before I apply by Reference the paper An Introduction of Latent Semantic Analysis by Peter Foltz and Darrel Laham.

In the paper “An Introduction to Latent Semantic Analysis” (LSA), Peter Foltz and Darrel Laham present a mathematical method for analyzing and understanding the relationships between words and documents. The goal of LSA is to identify latent (hidden) semantic relationships between words and documents, in order to improve the accuracy of information retrieval and other natural language processing tasks.

The authors begin by introducing the concept of a term-document matrix, which is a matrix of values that represents the relationships between words and documents. Each row of the matrix corresponds to a word, and each column corresponds to a document. The values in the matrix represent the frequency with which each word appears in each document.

The authors then describe how the term-document matrix can be reduced to a lower-dimensional space using singular value decomposition (SVD), which allows for the identification of latent semantic relationships between words and documents. They also describe how LSA can be used to identify synonyms and related terms, and how it can be applied to a variety of tasks, including information retrieval, text classification, and topic modeling.

Throughout the paper, the authors provide examples and case studies to illustrate the concepts and applications of LSA. They also discuss the limitations and potential pitfalls of using LSA, and suggest directions for future research.

Overall, the paper provides a comprehensive introduction to LSA and its applications, and is an important resource for researchers and practitioners interested in natural language processing and information retrieval.

Latent Semantic Analysis (LSA) is a mathematical method for analyzing and understanding the relationships between words and documents. It was first introduced in the paper “An Introduction to Latent Semantic Analysis” by Peter Foltz and Darrel Laham in 1998.

In LSA, a document is represented as a vector of word counts, and the relationships between words and documents are represented by a matrix of values known as a term-document matrix. This matrix is then reduced to a lower-dimensional space using singular value decomposition (SVD), which allows for the identification of latent (hidden) semantic relationships between words and documents.

One of the main applications of LSA is in information retrieval, where it can be used to improve the accuracy of search results by taking into account the relationships between words and documents. For example, LSA can be used to identify synonyms and related terms, which can help to improve the relevance of search results by expanding the set of terms that are used to match against a query.

In addition to its use in information retrieval, LSA has also been applied to a wide range of other tasks, including natural language processing, text classification, and topic modeling. It has proven to be a powerful and effective tool for understanding the meaning and context of text data.

Overall, LSA is a valuable method for analyzing and understanding the relationships between words and documents, and it continues to be widely used in a variety of applications.