http://debianws.lexgopc.com/wiki143/index.php?action=history&feed=atom&title=Semantic_analysis_%28machine_learning%29Semantic analysis (machine learning) - Revision history2026-05-15T16:48:34ZRevision history for this page on the wikiMediaWiki 1.43.1http://debianws.lexgopc.com/wiki143/index.php?title=Semantic_analysis_(machine_learning)&diff=5784590&oldid=previmported>Fadesga: /* References */2025-06-25T19:39:27Z<p><span class="autocomment">References</span></p>
<p><b>New page</b></p><div>{{Short description|Machine learning method for concept approximation}}<br />
{{other uses|Semantic analysis (disambiguation)}}<br />
{{More citations needed|date=January 2021}}{{Semantics}}<br />
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.<br />
<br />
Semantic analysis strategies include:<br />
<br />
* [[Metalanguage]]s based on [[first-order logic]], which can analyze the speech of humans.<ref name="IndurkhyaDamerau2010">{{cite book|author1=Nitin Indurkhya|author2=Fred J. Damerau|title=Handbook of Natural Language Processing|url=https://books.google.com/books?id=nK-QYHZ0-_gC|date=22 February 2010|publisher=CRC Press|isbn=978-1-4200-8593-8}}</ref>{{rp|93-}}<br />
* Understanding the semantics of a text is [[Symbol grounding problem|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.<ref name="Spranger2016">{{cite book|author=Michael Spranger|title=The evolution of grounded spatial language|url=https://books.google.com/books?id=z0VFDAAAQBAJ&pg=PA123|date=15 June 2016|publisher=Language Science Press|isbn=978-3-946234-14-2}}</ref>{{rp|123}}<br />
* [[Latent semantic analysis]] (LSA), a class of techniques where documents are represented as [[Feature (machine learning)#Feature vectors|vectors]] in a term space. A prominent example is [[probabilistic latent semantic analysis]] (PLSA).<br />
* [[Latent Dirichlet allocation]], which involves attributing document terms to topics.<br />
* [[n-gram]]s and [[hidden Markov model]]s, which work by representing the term stream as a [[Markov chain]], in which each term is derived from preceding terms.<br />
<br />
== See also ==<br />
* [[Explicit semantic analysis]]<br />
* [[Information extraction]]<br />
* [[Semantic similarity]]<br />
* [[Stochastic semantic analysis]]<br />
* [[Ontology learning]]<br />
<br />
== References ==<br />
{{reflist}}<br />
{{Natural language processing}}<br />
<br />
[[Category:Machine learning]]<br />
<br />
<br />
{{machine-learning-stub}}</div>imported>Fadesga