Semantic analysis (machine learning)
Throughout history, Semantic analysis (machine learning) has been a topic of constant interest to humanity. From ancient times to the modern era, Semantic analysis (machine learning) has captured the attention and curiosity of people of all cultures and nationalities. In this article, we will explore in depth all facets of Semantic analysis (machine learning), from its origins to its relevance today. Throughout the pages that follow, we will discover the importance of Semantic analysis (machine learning) in different contexts and how it has influenced the way we perceive the world around us. So join us on this journey through the fascinating world of Semantic analysis (machine learning).
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In machine learning, semantic analysis of a text 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.
Semantic analysis strategies include:
- Metalanguages based on first-order logic, which can analyze the speech of humans.[1]: 93-
- 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.[2]: 123
- Latent semantic analysis (LSA), a class of techniques where documents are represented as vectors in a term space. A prominent example is probabilistic latent semantic analysis (PLSA).
- Latent Dirichlet allocation, which involves attributing document terms to topics.
- n-grams and hidden Markov models, which work by representing the term stream as a Markov chain, in which each term is derived from preceding terms.
See also
- Explicit semantic analysis
- Information extraction
- Semantic similarity
- Stochastic semantic analysis
- Ontology learning
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.
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