Semantic analysis (machine learning)
Machine learning method for concept approximation
From Wikipedia, the free encyclopedia
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.
Stochastic semantic analysis
Stochastic semantic analysis is an approach used in computer science as a semantic component of natural language understanding.
Stochastic models generally use the definition of segments of words as basic semantic units for the semantic models, and in some cases involve a two layered approach.[3]
Example applications have a wide range. In machine translation, it has been applied to the translation of spontaneous conversational speech among different languages.[4] In the area of spoken language understanding the fact that spoken sentences often do not follow the grammar of a language and involve self-corrections, repetitions, and other irregularities, the use of stochastic semantic has been suggested as a natural fit to achieve robustness to deal with noise due to the spontaneous nature of spoken language.[5]
