Semantic analysis (machine learning)

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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-grams 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.

See also[edit]

  • Semantic analysis (machine learning)