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
- Semantic analysis (knowledge representation)
