Semantic analysis (machine learning)

In today's world, Semantic analysis (machine learning) has become a relevant issue that significantly impacts different areas of society. With the increase in globalization and the interconnection between cultures, Semantic analysis (machine learning) has acquired increasing relevance, generating debates and reflections that transcend borders and disciplines. In this article, we will explore the various facets of Semantic analysis (machine learning), analyzing its impact today and reflecting on its influence in the future. From a broad and interdisciplinary perspective, we will delve into the historical, social, political and cultural aspects of Semantic analysis (machine learning), with the aim of understanding its complexity and its implications in the contemporary world.

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Subfields
Computational Lexical (lexis, lexicology) Statistical Structural
Topics
Analysis Compositionality Context (language use) Prototype theory Force dynamics Semantic feature Semantic gap Theory of descriptions
Analysis
Latent Computational Machine-learning
Applications
Semantic file system Semantic desktop Semantic matching Semantic parsing Semantic similarity Semantic query Semantic Web Semantic wiki

In machine learning, semantic analysis of a 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. A metalanguage based on predicate logic can analyze the speech of humans.^: 93- Another strategy to understand 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.^: 123

Latent semantic analysis (sometimes latent semantic indexing), is a class of techniques where documents are represented as vectors in term space. A prominent example is PLSI.

Latent Dirichlet allocation involves attributing document terms to topics.

n-grams and hidden Markov models work by representing the term stream as a Markov chain where each term is derived from the few terms before it.

References

^ 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.

Natural language processing

General terms

Text analysis

Text segmentation	Compound-term processing Lemmatisation Lexical analysis Text chunking Stemming Sentence segmentation Word segmentation

Automatic summarization

Machine translation

Distributional semantics models

Language resources,
datasets and corpora

Types and standards	Corpus linguistics Lexical resource Linguistic Linked Open Data Machine-readable dictionary Parallel text PropBank Semantic network Simple Knowledge Organization System Speech corpus Text corpus Thesaurus (information retrieval) Treebank Universal Dependencies
Data	BabelNet Bank of English DBpedia FrameNet Google Ngram Viewer UBY WordNet

Automatic identification
and data capture

Topic model

Computer-assisted
reviewing

Natural language
user interface

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Semantic analysis (machine learning)

See also

References

Wikious

Boobota

Sagapedia