Collectively Representing Semi-Structured Data from the Web

Abstract

In this paper, we propose a single low dimensional representation of a large collection of table and hyponym data, and show that with a small number of primitive operations, this representation can be used effectively for many purposes. Specifically we consider queries like set expansion, class prediction etc. We evaluate our methods on publicly available semi-structured datasets from the Web.

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Document Details

Document Type
Technical Report
Publication Date
Jun 07, 2012
Accession Number
AD1046852

Entities

People

  • Bhavana Dalvi
  • Jamie Callan
  • William W. Cohen

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Acquisition
  • Air Force Research Laboratories
  • Algorithms
  • Automata Theory
  • Automatic
  • Computer Languages
  • Computer Science
  • Computers
  • Embedding
  • Extraction
  • Information Science
  • Language
  • Machine Learning
  • Schematic Diagrams
  • Semi-Supervised Learning
  • Supervised Machine Learning
  • Unsupervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Computational Linguistics
  • Geospatial Intelligence and Artificial Intelligence Analytics
  • Neural Network Machine Learning.