Text Corpus Semantic Analysis Task

A Text Corpus Semantic Analysis Task is a Semantic Analysis Task of that creates structure that approximate concepts from a large set of document.

• AKA: Semantic Analysis, Machine Learning Semantic Analysis Task.
• Example(s):
  • a Latent Semantic Analysis Task,
  • a Probabilistic Latent Semantic Indexing Task,
  • a Latent Semantic Indexing Task,
  • …
• Counter-Example(s):
  • a Syntactic Analysis Task.
• See: Knowledge Engineering Task, Knowledge Discovery Task, Semantic Web, Semantic Network, Markov Chain, Machine Learning, Metalanguage, Predicate Logic, Symbol Grounding, n-Gram, Hidden Markov Models, Information Extraction Task, Semantic Similarity Task, Ontology Learning Task.

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References

2021

• (Wikipedia, 2021) ⇒ https://en.wikipedia.org/wiki/Semantic_analysis_(machine_learning) Retrieved:2021-5-23.
  • 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. 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. 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.

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