Joint Probabilistic Reasoning About Coreference and Relations of Univeral Schema

Abstract

In this project, McCallums IESL lab at UMass Amherst researched and developed technologies for (1) automatic construction of knowledge bases from natural language text corpora, as well as (2) inference on these knowledge bases. Our work proposes and advances Universal Schema, which jointly learns embedded vector representations for the union of all input schema types (relation types, entity types, and entities themselves), including those from existing knowledge bases (such as Freebase and Wikipedia) as well as relations and types in natural language textual patterns. We present techniques for relation and type prediction based on matrix factorization.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 2017
Accession Number
AD1040958

Entities

People

  • Andrew McCallum
  • Nicholas Monath

Organizations

  • University of Massachusetts Amherst

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Air Force
  • Artificial Intelligence Software
  • Bayesian Networks
  • Computational Science
  • Computer Languages
  • Information Processing
  • Information Science
  • Information Systems
  • Language
  • Machine Learning
  • Named Entity Recognition
  • Natural Language Processing
  • Neural Networks
  • Ontologies
  • Probabilistic Models
  • Reasoning
  • Recurrent Neural Networks

Fields of Study

  • Computer science

Readers

  • Computational Linguistics
  • Information Retrieval
  • Technical Research and Report Writing.

Technology Areas

  • AI & ML
  • AI & ML - Information Retrieval