At times semantic concerns can reduce the number of interpretive possibilities of a sentence or section, but unfortunately at other times the semantic content of the characters can increase them.
Besides classification and clustering, we can note that semantic concern are present in tasks as information extraction [106 108], information retrieval [109 111], sentiment analysis [112 115], and automatic ontology building [116, 117], as well as the pre-processing step itself [118, 119].
Semantic concerns are left pragmatically in the hands of the user.
It is not a semantic thesis concerned with how humans represent the world in thought or language.
This recovery will be approximate when the semantic vector concerned encodes other predications, but the dissimilarity between elemental vectors makes it highly unlikely that the resulting vector will be closer to a random vector than it is to E esr1_protein).
In its broadest sense, statistical semantics is concerned with semantic properties of words, phrases, sentences, and texts, engendered by their distributional characteristics in large text corpora.
Properly formulated, it was a semantic principle concerning Ln that was statable only in its meta-language Ln+1.
For these philosophers, the central question about the right form for a semantic theory concerns the nature of these entities.
This interpretation covers several aspects—semantic ones (concerning the expressive power of a language), metaphysical ones (states of affairs being of one kind), and methodological ones.
And another nonreductive monist position has been motivated by appeal to a semantic thesis concerning how mental contents of propositional attitudes are determined.
There is a fundamental difference between semantic facts concerning belief contexts (as implication and synonymy), and questions that come closer to psychological processes (how can a person sincerely utter such a sentence?).
