I describe an implemented computational model of verbal aspect that supports the proposition that the semantics of aspect is grounded in sensory-motor primitives. In this theory, aspectual expressions refer to schematized piocesses that recur in sensory-motor control (such as goal, periodicity, iteration, final state, duration, and parameters such as force and effort). This active model of aspect grounded in sensory-motor primitives is able to model cross-linguistic variation in aspectual expressions while avoiding some paradoxes and problems in model-theoretic and other traditional accounts.

Human language processing relies on many kinds of linguistic knowledge, and is sensitive to their frequency, including lexical frequencies (Tyler, 1984; Salasoo & Pisoni, 1985; Marslen-Wilson, 1990; Zwiteerlood. 1989; Simpson & Burgess, 1985), idiom frequencies (d'Arcais, 1993). phonological neighborhood frequencies (Luce. Pisoni, & Goldfinger, 1990), subcategorization frequencies (Trueswell, Tanenhaus, & Kello, 1993), and thematic role frequencies (Trueswell, Tanenhaus, & Garnsey, 1994; Gamsey, Pearlmutter, Myers, & Lotocky, 1997). But while we know that each of these knowledge sources must be probabilistic, we know very little about exactly how these probabilistic knowledge sources are combined. This paper proposes the use of Bayesian decision trees in modeling the probabilistic, evidential nature of human sentence processing. Our method reifies conditional independence assertions implicit in sign-based linguistic theories and describes interactions among features without requiring additional assumptions about modularity. We show that our Bayesian approach successfully models psycholinguistic results on evidence combination in human lexical, idiomatic, and syntactic/semantic processing.

This paper describes results of a dynamic model of aspectual composition that demonstrates how features necessary for planning and controlling actions can also motivate and ground simple analyses of a number of aspectual phenomena. A novel feature of the model is an active computational representation for verb semantics called x-schemas, an extension of the Petri net formalism that can encode goals, resources and other features affecting aspect. Vexing problems of aspectual composition lend themselves to simple analyses in terms of the context-sensitive interaction between verb-specific x-schemas and a controller x-schema that captures important regularities in the evolution of events. The resulting x-schemas can be elaborated and constrained by such factors as tense, temporal modifiers, nominals and pragmatic context, providing a rich representation that supports simulative inference in language understanding.

This paper presents an implemented computational model of lexical development for the case of action verbs. A simulated agent is trained by an informant giving labels to the agent's actions (here hand motions] and the system learns to both label and carry out similar actions. Computationally, the system employs a novel form of active representation and is explicitly intended to be neurally plausible. The learning methodology is a version of Bayesian model merging (Omohundro, 1992). The verb learning model is placed in the broader context of the L0 project on embodied natural language and its acquisition.

This paper describes an implemented computational model of lexical development for the case of action verbs. A simulated agent is trained by an informant labeling the agent's actions (here hand motions), and the system learns to both label and carry out similar actions. The verb learning model is placed in the broader context of the NTL project on embodied natural language and its acquisition. Based on experimental results and projections to the full range of early lexemes, a significantly enriched model is proposed and its properties discussed.