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CONCLUSIONS

The definition of language has been suggested as a transformer between the two equivalent representations of information, the Text, i.e., the surface textual representation, and the Meaning, i.e., the deep semantic representation. This transformation is ambiguous in both directions: a homonymous Text corresponds to several different Meanings, and several synonymous Texts correspond to the same Meaning.

The description of the transformation process is greatly simplified by introducing intermediate levels of information representation, of which the main are morphologic and syntactic. At each level, some of the problems arising from synonymy and homonymy can be solved.

The general definitions of linguistic sign in Meaning Û Text Theory and in Head-driven Phrase Structure Grammar turned out to be in essence equivalent.

 

V. LINGUISTIC MODELS

THROUGHOUT THE PREVIOUS CHAPTERS, you have learned, on the one hand, that for many computer applications, detailed linguistic knowledge is necessary and, on the other hand, that natural language has a sophisticated structure, which is not easy to represent.

Thus, any application needs a description of language, i.e., the knowledge about its main properties. Such knowledge is organized in a model of language. The structure and degree of detail depend on the applications needs.

Our objectives now are to discuss the problem of modeling in computational linguistics. We observe the modeling in general, describe shortly the neurolinguistic and psycholinguistic models, and then discuss the functional models of natural language, with a special emphasis on common features of these models.


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  1. CONCLUSIONS
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  5. Conclusions
  6. General Conclusions
  7. LOGICAL CONCLUSIONS.
  8. The composition exercises ask you to manipulate data. They also expect you to make inferences in order to come to conclusions about the data.




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GENERATIVE, MTT, AND CONSTRAINT IDEAS IN COMPARISON | WHAT IS MODELING IN GENERAL?

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