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A Practical Approach for Representing Context and for Performing Word Sense Disambiguation Using Neural Networks.

Stephen I Gallant1

  • 1HNC, Inc., 49 Fenno Street, Cambridge, MA 02138 USA.

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This summary is machine-generated.

This study introduces context vectors and a dynamic context algorithm for natural language processing (NLP) to select correct word meanings. This computationally feasible method aims to improve NLP systems like machine translation.

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Area of Science:

  • Computational Linguistics
  • Artificial Intelligence
  • Machine Learning

Background:

  • Context representation is a significant challenge in natural language processing (NLP).
  • Previous approaches often lacked computational feasibility for large-scale systems.
  • Waltz and Pollack's work emphasized neutrally plausible systems.

Purpose of the Study:

  • To propose a computationally feasible method for representing context information in NLP.
  • To enable the selection of correct word meanings within sentences.
  • To encourage large-scale implementation and testing of context vector approaches.

Main Methods:

  • Introduction of context vectors for every word in an NLP system.
  • Development of a context algorithm to compute dynamic context vectors at any text position.
  • Utilizing neural network computations for meaning selection.

Main Results:

  • The proposed method provides a way to select correct word meanings by computing dynamic context vectors.
  • Context vectors offer a practical approach to handling context in NLP, overcoming previous difficulties.
  • Neural network learning algorithms can potentially enhance the accuracy of meaning selection.

Conclusions:

  • Context vectors offer a practical solution for a difficult NLP problem, improving word sense disambiguation.
  • The method is suitable for full-scale NLP applications such as machine translation and Japanese word processors.
  • Further research into more powerful context algorithms and full context vector set creation is encouraged.