What is Latent Semantic Analysis (LSI Indexing)?

[Patelsharp]

What is Latent Semantic Analysis (LSI Indexing).

[Garima Sharma]

LSI Indexing is a technique in natural language processing, in particular in vectorial semantics, of analyzing relationships between a set of documents and the terms they contain by producing a set of concepts related to the documents and terms.LSA closely approximates many aspects of human language learning and understanding.

[Mohit Rana]

Latent Semantic Analysis (LSA), also known as Latent Semantic Indexing (LSI), is a technique used in natural language processing and information retrieval to analyze relationships between a set of documents and the terms they contain. It aims to capture the underlying structure of meaning in the text by representing documents and terms as vectors in a high-dimensional space.

Here's how it typically works:

1. Constructing a Document-Term Matrix: LSA starts by constructing a matrix where rows represent documents and columns represent terms. Each cell in the matrix contains the frequency of a term in a document (or some other measure of ***ociation, such as TF-IDF scores).
2. Singular Value Decomposition (SVD): The next step involves applying singular value decomposition to this matrix. SVD is a mathematical technique that decomposes a matrix into three matrices: U, Σ, and V. The Σ matrix contains the singular values, which indicate the importance of the underlying concepts. By truncating this matrix to keep only the most significant singular values, LSA reduces the dimensionality of the data.
3. Reducing Dimensionality: After SVD, the matrix is transformed into a lower-dimensional space. This transformation retains the most important information while reducing noise and computational complexity.
4. Capturing Semantic Similarity: In the reduced-dimensional space, documents and terms are represented as vectors. LSA captures the semantic similarity between terms and documents by measuring the cosine similarity between their vectors. Terms and documents that are semantically related will have vectors that point in similar directions and thus have higher cosine similarities.
5. Application in Information Retrieval: LSA can be used for various tasks such as document cl***ification, clustering, and information retrieval. By representing documents and queries as vectors in the reduced-dimensional space, LSA can efficiently retrieve relevant documents based on their semantic similarity rather than just keyword matching.