A Knowledge based Computable Model for Reversible Grammar of the Spanish Language

J. Cardeñosa and C. Gallardo (Spain)


Artificial Intelligence, Knowledge Acquisition, Knowledge Representation, Linguistics


In this paper we present the design of a computable model of the Spanish Grammar that tries to overcome the problems presented by grammars based on linguistic theories. These problems are mainly the strictness of the formal model for the inclusion of new heterogeneous linguistic knowledge and an excessive dependency of the application. The proposed model facilitates the subsequent maintenance and scalability of the grammar. In particular, we will use the Descriptive Grammar of the Spanish Language for the creation of the computable through the observation, analysis and later synthesis of the knowledge contained in the descriptive grammar. The authors appeal at Knowledge Engineering methods and techniques for knowledge extraction, while “rescuing” the blackboard architecture as the reasoning and operative model for this approach.

Important Links:

Go Back