The networked home as a user-centric multimedia system
Proceedings of the 2004 ACM workshop on Next-generation residential broadband challenges
Journal of the American Society for Information Science and Technology
A resources virtualization approach supporting uniform access to heterogeneous grid resources
EUROCAST'07 Proceedings of the 11th international conference on Computer aided systems theory
Modeling Contexts with Dependent Types
Fundamenta Informaticae
Extraction of shallow language patterns: an approximation of data oriented parsing
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Cross-language patent matching via an international patent classification-based concept bridge
Journal of Information Science
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We describe a comprehensive framework for text understanding, based on the representation of context. It is designed to serve as a representation of semantics for the full range of interpretive and inferential needs of general natural language processing. Its most distinctive feature is its uniform representation of the various simple and independent linguistic sources that play a role in determining meaning: lexical associations, syntactic restrictions, case-role expectations, and most importantly, contextual effects. Compositional syntactic structure from a shallow parsing is represented in a neural net-based associative memory, where it then interacts through a Bayesian network with semantic associations and the context or "gist" of the passage carried forward from preceding sentences. Experiments with more than 2000 sentences in different languages are included.