Never-Ending Learning for Deep Understanding of Natural Language

Abstract

This research has explored the thesis that very significant amounts of background knowledge can lead to very substantial improvements in the accuracy of deep text analysis and understanding. To explore this thesis we have built on our earlier research on the Never Ending Language Learning (NELL) computer system, which has been running non-stop since January, 2010, learning to read the web, and automatically constructing a large knowledge base (aka knowledge graph) by extracting structured factual assertions from unstructured text on the web.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 2017
Accession Number
AD1040065

Entities

People

  • Tom M. Mitchell

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Accuracy
  • Air Force
  • Artificial Intelligence Software
  • Bayesian Networks
  • Computational Linguistics
  • Computational Science
  • Computer Languages
  • Computers
  • Information Science
  • Language
  • Linguistics
  • Machine Learning
  • Named Entity Recognition
  • Natural Language Processing
  • Natural Languages
  • Neural Networks
  • Ontologies

Fields of Study

  • Computer science

Readers

  • Artificial Intelligence
  • Distributed Systems and Data Platform Development
  • Systems Analysis and Design