Indexing by latent semantic analysis

Indexing By Latent Semantic Analysis

Indexing by latent semantic analysis is natural
language processing technique of vectorial semantics
that analyzes the relationship between documents and
the terms contained within. They also produce a set of
concepts related to the documents.

The new concepts of space from the latent semantic
indexing analysis can be used to compare the documents
in the concept space. This is also known as data
clustering or document classification.

They can be used to find similar documents across
languages, which is called cross language retrieval,
and can be used to find relations between terms, known
as synonymy and polysmemy.

Given a query of terms, the LSI analysis can be
translated into the concept space and find matching
documents. This is commonly known as information
retrieval.

But a fundamental problem with the synonymy and
polysemy is in the natural language processing.
Synonymy is where different words describe the same
idea.

A query in aRecommended For You