Concurrent Lexicalized Dependency Parsing: The ParseTalk Model

A grammar model for concurrent, object-oriented natural language parsing is introduced. Complete lexical distribution of grammatical knowledge is achieved building upon the head-oriented notions of valency and dependency, while inheritance mechanisms are used to capture lexical generalizations. The underlying concurrent computation model relies upon the actor paradigm. We consider message passing protocols for establishing dependency relations and ambiguity handling.Comment: 90kB, 7pages Postscrip

A layered abduction model of perception: Integrating bottom-up and top-down processing in a multi-sense agent

A layered-abduction model of perception is presented which unifies bottom-up and top-down processing in a single logical and information-processing framework. The process of interpreting the input from each sense is broken down into discrete layers of interpretation, where at each layer a best explanation hypothesis is formed of the data presented by the layer or layers below, with the help of information available laterally and from above. The formation of this hypothesis is treated as a problem of abductive inference, similar to diagnosis and theory formation. Thus this model brings a knowledge-based problem-solving approach to the analysis of perception, treating perception as a kind of compiled cognition. The bottom-up passing of information from layer to layer defines channels of information flow, which separate and converge in a specific way for any specific sense modality. Multi-modal perception occurs where channels converge from more than one sense. This model has not yet been implemented, though it is based on systems which have been successful in medical and mechanical diagnosis and medical test interpretation

A Conceptual Reasoning Approach to Textual Ellipsis

We present a hybrid text understanding methodology for the resolution of textual ellipsis. It integrates conceptual criteria (based on the well-formedness and conceptual strength of role chains in a terminological knowledge base) and functional constraints reflecting the utterances' information structure (based on the distinction between context-bound and unbound discourse elements). The methodological framework for text ellipsis resolution is the centering model that has been adapted to these constraints.Comment: 5 pages, uuencoded gzipped PS file (see also Technical Report at: http://www.coling.uni-freiburg.de/public/papers/ecai96.ps.gz

Integrating Marker Passing and Connectionism for Handling Conceptual and Structureal Ambiguities

This paper discusses the problem of selecting the correct knowledge structures in parsing natural language texts which are conceptually and structurally ambiguous and require dynamic reinterpretation. An approach to this problem is presented which represnets all knowledge structures in a uniform manner and which uses a constrained marker passing mechanism augmented with elements of connectionist models. This approach is shown to have the advantage of completely integrating all parsing processes, while maintatining a simple, domain-independedt processing mechanis

A Model of Schema Selection Using Marker Passing and Connectionist Spreading Activation

Schema selection involves determining which pre-stored schema best matches the current input.Traditional serial approaches utilize a match/predict cycle which is heavily dependent upon backtracking.This paper presents a parallel interactive model of schema selection called SAMPAN which is more flexible and adaptive. SAMPAN is a hybrid system that combines marker passing with connectionist spreading activation to provide a highly malleable and general representation for schema selection. This work is motivated by recent success in connectionist schema representations and in natural language marker passing systems. A connectionist schema representation provides many attractive features over traditional schema representations. However, a pure connectionist representation lacks generality; new propositions cannot easily be represented. SAMPAN gets around this problem by using marker passing to perform variable binding on generalized concepts. The S A M P A N system is a constraint satisfaction network with nodes that perform simple pattern matching and input summation. This approach is directly applicable to current schema-based systems