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Titolo:
A neural-network architecture for syntax analysis
Autore:
Chen, CH; Honavar, V;
Indirizzi:
Ind Technol Res Inst, Ctr Adv Technol, Comp & Commun Labs, Hsinchu, TaiwanInd Technol Res Inst Hsinchu Taiwan Comp & Commun Labs, Hsinchu, Taiwan Iowa0011te Univ, Dept Comp Sci, Artificial Intelligence Res Grp, Ames, IA 5 Iowa State Univ Ames IA USA 50011 ficial Intelligence Res Grp, Ames, IA 5
Titolo Testata:
IEEE TRANSACTIONS ON NEURAL NETWORKS
fascicolo: 1, volume: 10, anno: 1999,
pagine: 94 - 114
SICI:
1045-9227(199901)10:1<94:ANAFSA>2.0.ZU;2-P
Fonte:
ISI
Lingua:
ENG
Soggetto:
IMPLEMENTATION; DESIGN;
Keywords:
lexical analysis; modular neural networks; neural associative processing; neural associative processor; neural parser; neural symbolic processing; parsing; syntax analysis;
Tipo documento:
Review
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
Citazioni:
105
Recensione:
Indirizzi per estratti:
Indirizzo: Chen, CH Ind Technol Res Inst, Ctr Adv Technol, Comp & Commun Labs, Hsinchu, Taiwan Ind Technol Res Inst Hsinchu Taiwan mmun Labs, Hsinchu, Taiwan
Citazione:
C.H. Chen e V. Honavar, "A neural-network architecture for syntax analysis", IEEE NEURAL, 10(1), 1999, pp. 94-114

Abstract

Artificial neural networks (ANN's), due to their inherent parallelism, offer an attractive paradigm for implementation of symbol processing systems for applications in computer science and artificial intelligence. This paperexplores systematic synthesis of modular neural-network architectures for syntax analysis using a prespecified grammar-a prototypical symbol processing task which finds applications in programming language interpretation, syntax analysis of symbolic expressions, and high-performance compilers. The proposed architecture is assembled from ANN components for lexical analysis, stack, parsing and parse tree construction. Each of these modules takes advantage of parallel content-based pattern matching using a neural associative memory. The proposed neural-network architecture for syntax analysis provides a relatively efficient and high performance alternative to current computer systems for applications that involve parsing of LR grammars which constitute a widely used subset of deterministic context-free grammars. Comparison of quantitatively estimated performance of such a system [implemented using current CMOS very large scale integration (VLSI) technology] with that of conventional computers demonstrates the benefits of massively parallel neural-network architectures for symbol processing applications.

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Documento generato il 25/01/20 alle ore 18:24:44