Semantic analysis (machine learning)

<p>In <a href="/facts/Machine_learning/e0w0XJTu">machine learning</a>, semantic analysis of a <a href="/facts/Text_corpus/bYeEQQdh">text corpus</a> 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.
</p><p>Semantic analysis strategies include:
</p>
<ul><li><a href="/facts/Metalanguage/peNXFNUV">Metalanguages</a> based on <a href="/facts/First-order_logic/lcISqWIg">first-order logic</a>, which can analyze the speech of humans.: 93- </li>
<li>Understanding the semantics of a text is <a href="/facts/Symbol_grounding_problem/WmaaPW1G">symbol grounding</a>: 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.: 123 </li>
<li><a href="/facts/Latent_semantic_analysis/IugrMyMl">Latent semantic analysis</a> (LSA), a class of techniques where documents are represented as <a href="/facts/Feature_(machine_learning)/nzAhYxfu">vectors</a> in a term space. A prominent example is <a href="/facts/Probabilistic_latent_semantic_analysis/RehsIdWb">probabilistic latent semantic analysis</a> (PLSA).</li>
<li><a href="/facts/Latent_Dirichlet_allocation/BUWlwKWt">Latent Dirichlet allocation</a>, which involves attributing document terms to topics.</li>
<li><a href="/facts/N-gram/v4BT6rPP">n-grams</a> and <a href="/facts/Hidden_Markov_model/ur1zTAhP">hidden Markov models</a>, which work by representing the term stream as a <a href="/facts/Markov_chain/5oP5hntk">Markov chain</a>, in which each term is derived from preceding terms.</li></ul>

Semantic analysis (machine learning) open-in-new

Semantic analysis (machine learning)