Shallow Information Extraction for the knowledge Web

A new breed of Information Extraction tools has become popular and shown to be very effective in building massive-scale knowledge bases that fuel applications such as question answering and semantic search. These approaches rely on Web-scale probabilistic models populated through shallow language processing of the text, pre-existing knowledge, and structured data already on the Web. This tutorial provides an introduction to these techniques, starting from the foundations of information extraction, and covering some of its key applications.

• Publication year

  2013

• Venue

  IEEE International Conference on Data Engineering

• Publication date

  2013-04-01

• Fields of study

  Computer Science

• Identifiers
  DOI 10.1109/ICDE.2013.6544920
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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