- Source: Semantic analysis (machine learning)
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.: 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.: 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
Kata Kunci Pencarian:
- Studi kelayakan
- Radikal (Aksara Han)
- Linguistik komputasi
- Jaringan saraf konvolusional
- Bahasa hewan
- Graph database
- Mamalia
- Pengolahan bahasa alami
- Kecerdasan kolektif
- Pengenalan karakter optis
- Semantic analysis (machine learning)
- Semantic analysis (linguistics)
- Semantic analysis
- Outline of machine learning
- Semantic analysis (computational)
- Latent semantic analysis
- Machine learning
- Statistical learning theory
- Sentiment analysis
- Automated machine learning
