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Related Concept Videos

Neural Circuits01:25

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
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Components of Language01:24

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Language, whether spoken, signed, or written, consists of specific components: lexicon and grammar. The lexicon is the vocabulary of a language, comprising its words. Grammar is the set of rules used to convey meaning through the lexicon. For example, English grammar adds “-ed” to most verbs to indicate past tense. Words are formed by combining phonemes, which are the basic sound units of a language. Different languages have different sets of phonemes (e.g., “ah” vs. “eh”). Phonemes combine to...
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Neuron Structure01:31

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Related Experiment Videos

A neural-network architecture for syntax analysis.

C H Chen1, V Honavar

  • 1Advanced Technology Center, Computer and Communication Laboratories, Industrial Technology Research Institute, Chutung, Hsinchu, Taiwan, R.O.C.

IEEE Transactions on Neural Networks
|February 7, 2008
PubMed
Summary
This summary is machine-generated.

Artificial neural networks (ANNs) offer a parallel approach for symbol processing tasks like syntax analysis. This research presents a modular ANN architecture for efficient parsing of LR grammars, outperforming conventional systems.

Related Experiment Videos

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Computational Linguistics

Background:

  • Symbol processing is crucial for AI and computer science applications like compilers and programming language interpretation.
  • Traditional computer systems face challenges in efficiently handling complex parsing tasks.
  • Artificial neural networks (ANNs) offer inherent parallelism, making them suitable for computationally intensive symbol processing.

Purpose of the Study:

  • To explore the systematic synthesis of modular neural-network architectures for syntax analysis.
  • To develop an ANN-based system capable of parsing using a prespecified grammar, specifically LR grammars.
  • To evaluate the performance benefits of massively parallel neural-network architectures for symbol processing.

Main Methods:

  • The proposed architecture integrates modular ANNs for lexical analysis, stack operations, parsing, and parse tree construction.
  • Each module utilizes parallel content-based pattern matching via neural associative memory.
  • The system is designed for parsing LR grammars, a subset of deterministic context-free grammars.

Main Results:

  • A modular neural-network architecture for syntax analysis was systematically synthesized.
  • The architecture demonstrated efficient and high-performance parsing capabilities for LR grammars.
  • Quantitative performance estimations, particularly using CMOS VLSI technology, showed advantages over conventional computers.

Conclusions:

  • Massively parallel neural-network architectures present a viable and efficient alternative for symbol processing applications.
  • The proposed modular ANN approach offers significant performance benefits for syntax analysis and related tasks.
  • This research highlights the potential of ANNs in advancing high-performance compilers and programming language interpretation.