SemanticVectors System
A SemanticVectors System is a text-item vectorizing system.
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- Counter-Example(s):
- See: Text-Item Vector, Text-Item Vectorizng Model.
References
2014
- https://code.google.com/p/semanticvectors/
- QUOTE: SemanticVectors creates semantic WordSpace models from free natural language text. Such models are designed to represent words and documents in terms of underlying concepts. They can be used for many semantic (concept-aware) matching tasks such as automatic thesaurus generation, knowledge representation, and concept matching. These are described more thoroughly in the UseCases page. ...
... The models are created by applying concept mapping algorithms to term-document matrices created using Apache Lucene. The concept mapping algorithms supported by the package include Random Projection, Latent Semantic Analysis (LSA) and Reflective Random Indexing.
Random Projection is the most scalable technique in practice, because it does not rely on the use of computationally intensive matrix decomposition algorithms. The application of Random Projection for Natural Language Processing (NLP) is descended from Pentti Kanerva's work on Sparse Distributed Memory, which in semantic analysis and text mining, this method has also been called Random Indexing. Singular Value Decomposition is also popular because it is better known, and has in some cases given better results on smaller datasets.
- QUOTE: SemanticVectors creates semantic WordSpace models from free natural language text. Such models are designed to represent words and documents in terms of underlying concepts. They can be used for many semantic (concept-aware) matching tasks such as automatic thesaurus generation, knowledge representation, and concept matching. These are described more thoroughly in the UseCases page. ...