Shallow Information Extraction for the knowledge Web

Author

Barbosa, D. ; Haixun Wang ; Cong Yu

Author_Institution

Univ. of Alberta, Edmonton, AB, Canada

fYear

2013

fDate

8-12 April 2013

Firstpage

1264

Lastpage

1267

Abstract

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.

Keywords

Internet; knowledge based systems; natural language processing; question answering (information retrieval); text analysis; Web-scale probabilistic models; information extraction tools; knowledge Web; massive-scale knowledge bases; pre-existing knowledge; question answering; semantic search; shallow information extraction; shallow language processing; structured data; text processing; Data mining; Electronic publishing; Encyclopedias; Information retrieval; Knowledge based systems; Semantics;

fLanguage

English

Publisher

ieee

Conference_Titel

Data Engineering (ICDE), 2013 IEEE 29th International Conference on

Conference_Location

Brisbane, QLD

ISSN

1063-6382

Print_ISBN

978-1-4673-4909-3

Electronic_ISBN

1063-6382

Type

conf

DOI

10.1109/ICDE.2013.6544920

Filename

6544920