In
machine learning
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of Computational statistics, statistical algorithms that can learn from data and generalise to unseen data, and thus perform Task ( ...
, semantic analysis of a
text corpus
In linguistics and natural language processing, a corpus (: corpora) or text corpus is a dataset, consisting of natively digital and older, digitalized, language resources, either annotated or unannotated.
Annotated, they have been used in corp ...
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:
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Metalanguages based on
first-order logic
First-order logic, also called predicate logic, predicate calculus, or quantificational logic, is a collection of formal systems used in mathematics, philosophy, linguistics, and computer science. First-order logic uses quantified variables over ...
, 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.
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Latent semantic analysis
Latent semantic analysis (LSA) is a technique in natural language processing, in particular distributional semantics, of analyzing relationships between a set of documents and the terms they contain by producing a set of concepts related to the d ...
(LSA), a class of techniques where documents are represented as
vectors in a term space. A prominent example is
probabilistic latent semantic analysis Probabilistic latent semantic analysis (PLSA), also known as probabilistic latent semantic indexing (PLSI, especially in information retrieval circles) is a statistical technique for the analysis of two-mode and co-occurrence data. In effect, one c ...
(PLSA).
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Latent Dirichlet allocation
In natural language processing, latent Dirichlet allocation (LDA) is a Bayesian network (and, therefore, a generative statistical model) for modeling automatically extracted topics in textual corpora. The LDA is an example of a Bayesian topic ...
, which involves attributing document terms to topics.
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n-grams and
hidden Markov models, which work by representing the term stream as a
Markov chain
In probability theory and statistics, a Markov chain or Markov process is a stochastic process describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event. Informally ...
, in which each term is derived from preceding terms.
See also
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Explicit semantic analysis In natural language processing and information retrieval, explicit semantic analysis (ESA) is a Vector space model, vectoral representation of text (individual words or entire documents) that uses a document corpus as a knowledge base. Specifically, ...
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Information extraction
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Semantic similarity
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Stochastic semantic analysis
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Ontology learning
References
Machine learning
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