Distributed Vector Representations of Words in the Sigma Cognitive Architecture

Abstract

Recently reported results with distributed-vector word representations in natural language processing make them appealing for incorporation into a general cognitive architecture like Sigma. This paper describes a new algorithm for learning such word representations from large, shallow information resources, and how this algorithm can be implemented via small modifications to Sigma. The effectiveness and speed of the algorithm are evaluated via a comparison of an external simulation of it with state-of-the-art algorithms. The results from more limited experiments with Sigma are also promising, but more work is required for it to reach the effectiveness and speed of the simulation.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2014
Accession Number
AD1158406

Entities

People

  • Abram Demski
  • Kenji Sagae
  • Paul Simon Rosenbloom
  • Volkan Ustun

Organizations

  • University of Southern California

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Cognitive Science
  • Composite Materials
  • Computations
  • Computer Languages
  • Computer Science
  • Data Sets
  • Information Processing
  • Information Systems
  • Language
  • Machine Learning
  • Natural Language Processing
  • Natural Languages
  • Simulations
  • Vector Spaces

Fields of Study

  • Computer science

Readers

  • Approximation Theory.
  • Computational Linguistics
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Machine Translation