Scalable Prosodic, Anomaly and Relational Knowledge Exploration of Language with Enhanced Robustness (SPARKLER)

Abstract

We have developed trilingual (English, Chinese and Spanish) open-domain knowledge extraction techniques to extract, populate, and analyze unstructured data from heterogeneous sources in to acknowledge base, to relieve the reliance on manual knowledge base construction or in-person communication for knowledge management. This new frame work is able to discover schemes and extract facts from any input data in any domain, without any annotated training data, by incorporating distributional semantics and symbolic semantics. The resulting systems have won top performance at several NIST international research evaluations and been selected for DARPA and ARL demos and transitions. 4 PhD students have been supported, and 54 papers have been published at top conferences and journals.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 2018
Accession Number
AD1052273

Entities

People

  • Andrew Rosenberg
  • Heng Ji
  • Yang Liu

Organizations

  • Research Foundation of The City University of New York

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Air Force
  • Artificial Intelligence Software
  • Automated Speech Recognition
  • Computational Linguistics
  • Computational Science
  • Computer Languages
  • Data Mining
  • Detection
  • Information Science
  • Information Systems
  • Language
  • Machine Learning
  • Named Entity Recognition
  • Natural Language Processing
  • Neural Networks
  • Online Communications
  • Social Media

Readers

  • Computational Linguistics
  • Database Systems and Applications
  • Technical Research and Report Writing.