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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.
• 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.
• 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

1. Nitin Indurkhya; Fred J. Damerau (22 February 2010). Handbook of Natural Language Processing. CRC Press. ISBN 978-1-4200-8593-8.
2. Michael Spranger (15 June 2016). The evolution of grounded spatial language. Language Science Press. ISBN 978-3-946234-14-2.

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