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Báo cáo khoa học: "From HOPE en I'ESPERANCE On the Role of Computational Neurolinguistics in Cross-Language Studies "
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Báo cáo khoa học: "From HOPE en I'ESPERANCE On the Role of Computational Neurolinguistics in Cross-Language Studies "
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Computational neurolinguistics (CN) is an approach to computational l i n g u i s t i c s which includes neurally-motivated constraints in the design of models of natural language processing. Furthermore, the knowledge representations included in such models must be supported with documented behaviorial e v ~ c e , normal and pathological. This paper w i l l discuss the contribution of CN models to ~the understanding of l i n g u i s t i c "competence" within recent research efforts to adapt HOPE (Gigley 1981; 1982a; 1982b; 1982c; 1983a), an implemented CN. | From HOPE en 1 ESPERANCE On the Role of Computational Neurolinguistics in Cross-Language Studies 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 includes neurally-motivated constraints in the design of models of natural language processing. Furthermore the knowledge representations included in such models must be supported with documented behaviorial evidence normal and pathological . 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 understanding English to 1 ESPERANCE one which understands French. 1. INTRODUCTION Computational Neurolinguistics CN incorporates 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 discrimination and recognition of patterns employing associative network models that have been tuned or have adaptively learned to relate certain discriminations Gordon 1982 Wood 1978 1980 . The research described in this paper was supported by an NIH-CNRS research exchange grant entitled .
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