From HOPE en I’ESPERANCE On the Role of Computational Neurolinguistics in Cross-Language Studies

From HOPE en I'ESPERANCE On the Role of Computational Neurolinguistics in Cross-Language Studies I Helen M. Gigley Department of Computer Science University of New Hampshire Durham, NH 03824 ABSTRACT Computational neurolinguistics (CN) is an approach to computational linguistics which in- cludes neurally-motivated constraints in the design of models of natural language processing. Furthermore, the knowledge representations in- cluded in such models must be supported with documented behaviorial ev~ce, normal and patho- logical. This paper will discuss the contribution of CN models to ~the understanding of linguistic "competence" within recent research efforts to adapt HOPE (Gigley 1981; 1982a; 1982b; 1982c; 1983a), an implemented CN model for "under- standing" English to I'ESPERANCE, one which "un- derstands" French. I. INTRODUCTION Computational Neurolinguistics (CN) incorpor- ates initial assumptions about language processing that are often indirectly referenced in other computational approaches to language study. These assumptions focus on neural-like computational mechanisms (Ballard 1982; Feldman 1981; Gigley, 1982a; 1982b; 1983a; McClelland and Rumelhart, 1981) which subserve language behavior (Lavorel and Gigley, 1983). Furthermore, CN approaches to different aspects of language processing include extensive use of behavioral data. Research exists within the CN paradigm along various behaviorally defined dimensions. These are at the level of phonetic speech studies that simulate speech errors (Le- cours and Lhermitte, 1969; Reggia and Sanjeev, 1984), a model of aphasic language production, JARGONAUT, (Lavorel, 1982), as well as within lesionable models at a neural network level. These latter models simulate association, dis- crimination, and recognition of patterns employing associative network models that have been tuned or have adaptively learned to relate certain dis- criminations (Gordon, 1982; Wood, 1978; 1980). IThe research described in this paper was sup- ported by an NIH-CNRS research exchange grant entitled "Computational Neurolinguistics" and was undertaken at Laboratoire de Neuropsychologie Exp~rimentale, INSERM-Unit~ 94, BRON, France. There is much philosophical and linguistic discussion of the nature of the representations that exist in humans and form the basis of our cognitive function. We will not present the debate here, but instead will claim that the CN models we build include the assumption that the internal representation of concepts, words, and phonemes are given by the overall activation state of the "network" representation within the system at a moment in time. Furthermore, this means that unless activations are interpreted externally (in our case by labels so that we can talk about them), they in and of themselves reflect the "mental" representation. To this end, CN models present time- synchronized snapshots of an interactive, paral- lel, distributed process that are interpreted to represent hierarchies of linguistic knowledge that can be distinguished during processing, such as a recognized word, a grammatical interaction, or even a disambiguated meaning. Before turning to our efforts to adapt a working implementation within the CN paradigm, HOPE, into one that can process French with equal facility, I'ESPERANCE, we will present necessafy background to illustrate why focusing on the "process" of language, as it can exist, based on our current understanding of brain function, contributes significantly to our increased …

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