Hume: Domain-Agnostic Extraction of Causal Analysis Graphs

Abstract

Report developed under contract W911NF-18-C-003. Raytheon BBN developed Hume, a system which builds qualitative, causal analysis graphs (CAGs) by reading text (textbooks, academic literature, government/other reports, encyclopedias, news and online sources). Specifically, during this effort we: -Developed tools that extract events and causal factors from text using Deep Neural Networks (DNN). -Developed tools that extract casual relations from text using DNN. The causal relation types are intended to be domain-agnostic. -Developed clustering tools to aid analysts in constructing and enriching ontologies in new domains. -Participated in internal evaluations, technology assessments, and collaborative experiments.

Document Details

Document Type
Technical Report
Publication Date
Mar 31, 2022
Accession Number
AD1189441

Entities

People

  • Bonan Min
  • Jessica Macbride
  • Yee S Chan

Organizations

  • RTX

Tags

Communities of Interest

  • Energy and Power Technologies
  • Human Systems

DTIC Thesaurus Topics

  • Abstracts
  • Agreements
  • Artificial Intelligence Software
  • Classification
  • Computational Linguistics
  • Computational Science
  • Computer Languages
  • Convolutional Neural Networks
  • Data Mining
  • Data Sets
  • Governments
  • Health Services
  • Information Processing
  • Information Systems
  • Language
  • Linguistics
  • Machine Learning
  • Natural Language Processing
  • Neural Networks
  • Ontologies
  • Standards
  • Technology Assessment

Readers

  • Library and Information Science
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Information Retrieval
  • AI & ML - Neural Networks