Self-Organizing Grammar Induction Using a Neural Network Model

This paper presents a self-organizing, real-time, hierarchical neural network model of sequential processing, and shows how it can be used to induce recognition codes corresponding to word categories and elementary grammatical structures. The model, first introduced in Mannes (1992), learns to recognize, store, and recall sequences of unitized patterns in a stable manner, either using short-term memory alone, or using long-term memory weights. Memory capacity is only limited by the number of nodes provided. Sequences are mapped to unitized patterns, making the model suitable for hierarchical operation. By using multiple modules arranged in a hierarchy and a simple mapping between output of lower levels and the input of higher levels, the induction of codes representing word category and simple phrase structures is an emergent property of the model. Simulation results are reported to illustrate this behavior.National Science Foundation (IRI-9024877

On existential declarations of independence in IF Logic

We analyze the behaviour of declarations of independence between existential quantifiers in quantifier prefixes of IF sentences; we give a syntactical criterion for deciding whether a sentence beginning with such prefix exists such that its truth values may be affected by removal of the declaration of independence. We extend the result also to equilibrium semantics values for undetermined IF sentences. The main theorem allows us to describe the behaviour of various particular classes of quantifier prefixes, and to prove as a remarkable corollary that all existential IF sentences are equivalent to first-order sentences. As a further consequence, we prove that the fragment of IF sentences with knowledge memory has only first-order expressive power (up to truth equivalence)

Artequakt: Generating tailored biographies from automatically annotated fragments from the web

The Artequakt project seeks to automatically generate narrativebiographies of artists from knowledge that has been extracted from the Web and maintained in a knowledge base. An overview of the system architecture is presented here and the three key components of that architecture are explained in detail, namely knowledge extraction, information management and biography construction. Conclusions are drawn from the initial experiences of the project and future progress is detailed