Movement in Active Production Networks

MOVEMENT IN ACTIVE PRODUCTION NETWORKS Mark A. Jones Alan S. Driacoll AT&T Bell Laboratories Murray Hill, New Jersey 07974 ABSTRACT We describe how movement is handled in a class of computational devices called active production networks (APNs). The APN model is a parallel, activation-basod framework that ha= been applied to other aspects of natural language processing. The model is briefly defined, the notation and mechanism for movement is explained, and then several examples are given which illustrate how various conditions on movement can naturally be explained in terms of limitations of the APN device. I. INTRODUCTION Movement is an important phenomenon in natural languages. Recently, proposals such as Gazdar's dcrivod rules (Gazdar, 1982) and Pereira's extraposition grammars (Pereirao 1983) have attemptod to find minimal extensions to the context-free framework that would allow the descrip- tion of movement. In this paper, we describe a class of computational devices for natural language processing. called active production networks (APNs), and explore how certain kinds of movement are handled. In particular. we are concerned with left extraposition, such as Subject- auxiliary Inversion. Wh-movement, and NP holes in rela- tive clauses, in these cos•s, the extraposod constituent leaves a trace which is insertod at a later point in the pro- cessing. This paper builds on the research reported in Jones (1983) and Jones (forthcoming). 7,. ACTIVE PRODUCTION NgrwoPJ~ 7..1 Tim i~vk~ Our contention is that only a class of parallel devices will prove to be powerful enough to allow broad contextual priming, to pursue alternative hypotheses, and to explain the paradox that the performance of a sequential system often degrades with new knowledge, whereas human per- formance usually improves with learning and experience. = There are a number of new parallel processing (connection- • st) models which are sympathetic to this view--Anderson (1983). Feldman and Ballard (1982), Waltz and Pollack (1985). McClelland and Rumelhart (1981, 1982), and Fahlman. Hinton and Sejnowski (1983). Many of the connection•st models use iterative relaxa- tion techniques with networks containing excitatory and inhibitory links. They have primarily been used as best-fit categorizers in large recognition spaces, and it is not yet clear how they will implement the rule-governed behavior of parsers or problem solvers. Rule-based systems need a strong notion of an operating state, and they depend heavily on appropriate variable binding schemes for opera- tions such as matching (e.g.. unification) and recurs•on. The APN model directly supports a rule-based interpreta- tion, while retaining much of the general flavor of I. 1"be htmmm li~ity to L:mrfofm mmlpatztmmtlly e•patm,m opmltmm =alia s ~ y ~, imt'alkd loud,mum remforou this b¢fid. connection•sin. An active production network is a rule- oriented, distributed processing system based on the follow- ing principles: 1. Each node in the network executes a uniform activa- tion algorithm and assumes states in response to mes- sage (,such as expectation, inhibition, and activation) that arrive locally; the node can, in turn, relay mes- sages, initiate messages, and spawn new instances to process message activity. Although the patterns that define a …

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