Learning Verbal Transitivity Using LogLinear Models
ECML '98 Proceedings of the 10th European Conference on Machine Learning
EPIA '99 Proceedings of the 9th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
TALISMAN: A Multi-Agent System for Natuarl Language Processing
SBIA '95 Proceedings of the 12th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
Integrating Morphological, Syntactical and Sementical Aspects through Multi-agent Cooperation
SBIA '98 Proceedings of the 14th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
Using Loglinear Clustering for Subcategorization Identification
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
A SOMAgent for machine translation
Expert Systems with Applications: An International Journal
Short communication: A SomAgent statistical machine translation
Applied Soft Computing
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We present in this paper some aspects concerning the design and implementation of an architecture that is the basis for the development of a natural language processing system that, besides the obvious goal of building some computational representation (at a desired level) of the input, has two main objectives: to be robust and to evolve. To be robust in the sense that the non recognition of some input should not block the system but, instead, should lead the system to an automatic recovery process. To evolve, so that when some incompleteness/incorrectness is detected (or suspected) during a recovery process, the component responsible for the mistake should be updated accordingly, so that in future analogous situations the system can perform better. In order to achieve this goal we propose the definition of a distributed architecture.