Semantic analysis (machine learning)

Template:Short description Template:Semantics In machine learning, semantic analysis of a text 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.

Semantic analysis strategies include:

• Metalanguages based on first-order logic, which can analyze the speech of humans.Template:Rp
• Understanding the semantics of a text is symbol grounding: if language is grounded, it is equal to recognizing a machine-readable meaning. For the restricted domain of spatial analysis, a computer-based language understanding system was demonstrated.Template:Rp
• Latent semantic analysis (LSA), a class of techniques where documents are represented as vectors in a term space. A prominent example is probabilistic latent semantic analysis (PLSA).
• Latent Dirichlet allocation, which involves attributing document terms to topics.
• n-grams and hidden Markov models, which work by representing the term stream as a Markov chain, in which each term is derived from preceding terms.

See also

• Explicit semantic analysis
• Information extraction
• Semantic similarity
• Stochastic semantic analysis
• Ontology learning

References

Template:Machine-learning-stub