The ParseTalk model of concurrent, object-oriented natural language parsing is introduced. It builds upon the complete lexical distribution of grammatical knowledge and incorporates inheritance mechanisms in order to express generalizations over sets of lexical items. The grammar model integrates declarative well-formedness criteria constraining linguistic relations between heads and modifiers, and procedural specifications of the communication protocol for establishing these relations. The parser's computation model relies upon the actor paradigm, with concurrency entering through asynchronous message passing. We consider various extensions of the basic actor model as required for distributed natural language understanding and elaborate on the semantics of the actor computation model in terms of event type networks (a graph representation for actor grammar specifications) and event networks (graphs which represent the actor parser's behavior). Besides theoretical claims, we present an interactive grammar/parser workbench, a graphical development environment with various types of browsers, tracers, inspectors and debuggers that has been adapted to the requirements of large-scale grammar engineering in a distributed, object-oriented specification and programming framework.

Abstract. 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. 1

The cognitive modeling community is presently divided between two different approaches to the spread of activation through networks. One school holds that symbolic information must be progagated, the other that numeric weights are used and activation spreads in a more analog manner. In this paper we describe a mechanism which allows the two processes to be merged via the introduction into the symbolic network of a defining-characteristic link which is affects the spread of the symbolic information in a manner resembling local connectionist computations. We demonstrate that the combined system is more powerful than either of the separate models alone.

This paper describes EUREKA, aproblem-solving architecture that operates under strong constraints on its memory and processes. Most significantly, EUREKA does not assume free access to its entire long-term memory. That is, failures in problem solving may arise not only from missing knowledge, but from the (possibly temporary) inability to retrieve appropriate existing knowledge from memory. Additionally, the architecture does not include systematic backtracking to recover from fruitless search paths. These constraints significantly impact EUREKA’s design. Humans are also subject to such constraints, but are able to overcome them to solve problems effectively. In EUREKA’s design, we have attempted to minimize the number of additional architectural commitments, while staying faithful to the memory constraints. Even under such minimal commitments, EUREKA provides a qualitative account of the primary types of learning reported in the literature on human problem solving. Further commitments to the architecture would refine the details in the model, but the approach we have taken de-emphasizes highly detailed modeling to get at general root causes of the observed regularities. Making minimal additional commitments to EUREKA’s design strengthens the case that many regularities in human learning and problem solving are entailments of the need to handle imperfect memory.

In this paper, we present a model of anaphor resolution within the framework of the centering model. The consideration of an incremental processing mode introduces the need to manage structural ambiguity at the center level. Hence, the centering framework is further refined to account for local and global parsing ambiguities which propagate up to the level of center representations, yielding moderately adapted data structures for the centering algorithm. Introduction Psycholinguistic studies have revealed ample evidence for the incrementality of human language comprehension, not only at the phrasal and clausal level but also at the discourse level of anaphora resolution (Just & Carpenter, 1987; Sanford & Garrod, 1989). Correspondingly, incremental processing has also become a major challenge for cognitively plausible, computational models of natural language understanding (Jurafsky, 1992; Sturt, 1995), and text understanding (Granger et al., 1986) in particular. Introducing incrementa...

The Knowledge-Based Machine Translation paradigm requires a comprehensive analysis of input texts into an unambiguous machine-tractable representation of the propositional and meta-propositional meaning of that text, for which we use a particular framework referred to as ontological semantics. The work presented here begins with a definition of a representation language for lexical semantic specification (and syntax/semantics interface) to support such an analysis, as well as a generalized algorithm for building the meaning representation from these lexical semantic specifications, utilizing the ontology and a syntactic parse as knowledge sources. The core of the algorithm is an algorithm for semantic constraint satisfaction and relaxation, involving finding the best path over the ontology between a candidate filler of a relation and semantic constraints on that relation. The ontology is viewed as a multi-dimensional graph, with distinct topologies in each dimension reflecting specific semantic relations between nodes (representing concepts) , where weights or arc distance reflects strength of semantic relatedness in context (where the path-so-far context is maintained in a state transition table).

The ParseTalk model of concurrent, object-oriented natural language parsing is introduced. It builds upon the complete lexical distribution of grammatical knowledge and incorporates inheritance mechanisms in order to express generalizations over sets of lexical items. The grammar model integrates declarative well-formedness criteria constraining linguistic relations between heads and modifiers, and procedural specifications of the communication protocol for establishing these relations. The parser's computation model relies upon the actor paradigm, with concurrency entering through asynchronous message passing. We consider various extensions of the basic actor model as required for distributed natural language understanding and elaborate on the semantics of the actor computation model in terms of event type networks (a graph representation for actor grammar specifications) and event networks (graphs which represent the actor parser's behavior). Besides theoretical claims, we present an interactive grammar/parser workbench, a graphical development environment with various types of browsers, tracers, inspectors and debuggers that has been adapted to the requirements of large-scale grammar engineering in a distributed, object-oriented specification and programming framework. 2 1