Composing Information, Extraction, Semantic Parsing and Tractable Inference for Deep NLP

Abstract

We developed new information extraction technologies. Our Vinculum entity linker is simple and modular; we compare it to other top systems analyze approaches to mention extraction, candidate generation, entity type prediction, entity coreference,and coherence. We also developed both unsupervised and semi-supervised algorithms for event extraction that exploit parallel news streams, showing significant performance improvements on multiple event extractors over ACE2005 and TAC-KBP 2015 datasets. Finally, we developed new natural language processing tools (e.g., semantic parsing) and introduced efficient inference algorithms for extracted knowledge bases.

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Document Details

Document Type
Technical Report
Publication Date
May 24, 2018
Accession Number
AD1052274

Entities

People

  • Daniel S. Weld
  • Hannaneh Hajishirzi
  • Luke Zettlemoyer
  • Pedro Domingos

Organizations

  • University of Washington

Tags

Communities of Interest

  • Autonomy
  • Human Systems

DTIC Thesaurus Topics

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

Fields of Study

  • Computer science

Readers

  • Computational Linguistics
  • Distributed Systems and Data Platform Development

Technology Areas

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