Semantic analysis (machine learning)
Encyclopedia
In machine learning, semantic analysis of a corpus is the task of building structures that approximate concepts from a large set of documents. It generally does not involve prior semantic understanding of the documents.
Latent semantic analysis
(sometimes latent semantic indexing), is a class of techniques where documents are represented as vectors
in term space. A prominent example is PLSI.
Latent Dirichlet allocation
involves attributing document terms to topics.
n-gram
s and hidden Markov models work by representing the term stream as a markov chain
where each term is derived from the few terms before it.
Latent semantic analysis
Latent semantic analysis
Latent semantic analysis 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 assumes that words that are close...
(sometimes latent semantic indexing), is a class of techniques where documents are represented as vectors
Linear algebra
Linear algebra is a branch of mathematics that studies vector spaces, also called linear spaces, along with linear functions that input one vector and output another. Such functions are called linear maps and can be represented by matrices if a basis is given. Thus matrix theory is often...
in term space. A prominent example is PLSI.
Latent Dirichlet allocation
Latent Dirichlet allocation
In statistics, latent Dirichlet allocation is a generative model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar...
involves attributing document terms to topics.
n-gram
N-gram
In the fields of computational linguistics and probability, an n-gram is a contiguous sequence of n items from a given sequence of text or speech. The items in question can be phonemes, syllables, letters, words or base pairs according to the application...
s and hidden Markov models work by representing the term stream as a markov chain
Markov chain
A Markov chain, named after Andrey Markov, is a mathematical system that undergoes transitions from one state to another, between a finite or countable number of possible states. It is a random process characterized as memoryless: the next state depends only on the current state and not on the...
where each term is derived from the few terms before it.