Even if words are meaningful only because they occur as constituents within sentences, there could still be a function (perhaps even a single function across all possible human languages) that maps the structure of a sentence and the meanings of its constituent words to the meaning of that sentence.
In a sentence, concept mapping in Leximancer is about understanding semantic relationships within the entire network of concepts that exist in a language dataset.
This was chosen as the first model because it uses the paragraph as a memory to keep the order of the words in a sentence, and maps paragraphs, as well as words, to a vector.
In natural language generation (NLG) systems, surface realisation is known as the task of mapping abstract sentence representations to a surface form, that is, a sequence of words, punctuation symbols etc., to be delivered to the document presentation system [1].
One indicates what a sentence is about by mapping out logical space according to its changing ways of being true or false.
Following an initial mapping sentence outlining the possible dimensions of SL, we employed a battery of SL tasks in the visual and auditory modalities, using verbal and non-verbal stimuli, with adjacent and non-adjacent contingencies.
Surface realisation can be viewed as the task of mapping abstract sentence representations to word strings in a target language.
In a sentence: A good, cheap way to use OS maps.
Map the country and correctly locate at least ten volunteer projects, describing their aims in a sentence or two.
Using a tree representation, the structure of a sentence can be clearly shown by applying PSG rules at each level of the tree to map sentence elements at the current level to the sentence elements at a higher level.
