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.


This article uses material from the Wikipedia article Semantic analysis (machine learning), which is released under the Creative Commons Attribution-Share-Alike License 3.0.