In
machine learning
Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence.
Machine ...
, semantic analysis of a
corpus
Corpus is Latin for "body". It may refer to:
Linguistics
* Text corpus, in linguistics, a large and structured set of texts
* Speech corpus, in linguistics, a large set of speech audio files
* Corpus linguistics, a branch of linguistics
Music
* ...
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
In logic and linguistics, a metalanguage is a language used to describe another language, often called the ''object language''. Expressions in a metalanguage are often distinguished from those in the object language by the use of italics, quota ...
based on
predicate logic can analyze the speech of humans.
Another strategy to understand the semantics of a text is
symbol grounding
In cognitive science and semantics, the symbol grounding problem concerns how it is that words ( symbols in general) get their meanings, and hence is closely related to the problem of what meaning itself really is. The problem of meaning is i ...
. 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.
(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-gram
In the fields of computational linguistics and probability, an ''n''-gram (sometimes also called Q-gram) is a contiguous sequence of ''n'' items from a given sample of text or speech. The items can be phonemes, syllables, letters, words or ...
s and
hidden Markov models
A hidden Markov model (HMM) is a statistical Markov model in which the system being modeled is assumed to be a Markov process — call it X — with unobservable ("''hidden''") states. As part of the definition, HMM requires that there be an ...
work by representing the term stream as a
Markov chain
A Markov chain or Markov process is a stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event. Informally, this may be thought of as, "What happen ...
where each term is derived from the few terms before it.
See also
*
Explicit semantic analysis In natural language processing and information retrieval, explicit semantic analysis (ESA) is a vectoral representation of text (individual words or entire documents) that uses a document corpus as a knowledge base. Specifically, in ESA, a word is ...
*
Information extraction
Information extraction (IE) is the task of automatically extracting structured information from unstructured and/or semi-structured machine-readable documents and other electronically represented sources. In most of the cases this activity concer ...
*
Semantic similarity
*
*
Ontology learning
Ontology learning (ontology extraction, ontology generation, or ontology acquisition) is the automatic or semi-automatic creation of ontologies, including extracting the corresponding domain's terms and the relationships between the concepts tha ...
References
Machine learning
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