&sq=&lang=&file=File:Question_book-new.svg){kind=small} | This article needs additional citations for verification. (January 2021) |
| Semantics | ||||||||
|---|---|---|---|---|---|---|---|---|
|
||||||||
|
Semantics of programming languages | ||||||||
|
||||||||
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.[1]: 93-
- 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.[2]: 123
- 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.
- ^ Nitin Indurkhya; Fred J. Damerau (22 February 2010). Handbook of Natural Language Processing. CRC Press. ISBN 978-1-4200-8593-8.
- ^ Michael Spranger (15 June 2016). The evolution of grounded spatial language. Language Science Press. ISBN 978-3-946234-14-2.