Networks and Natural Language Processing

Description:

Article discussing networks and natural language processing. The authors present some of the most successful graph-based representations and algorithms used in language processing and try to explain how and why they work.

Creator(s):
Creation Date: September 2008
Partner(s):
UNT College of Engineering
Collection(s):
UNT Scholarly Works
Usage:
Total Uses: 66
Past 30 days: 1
Yesterday: 0
Creator (Author):
Radev, Dragomir R.

University of Michigan

Creator (Author):
Mihalcea, Rada, 1974-

University of North Texas

Publisher Info:
Place of Publication: [Palo Alto, California]
Date(s):
  • Creation: September 2008
Description:

Article discussing networks and natural language processing. The authors present some of the most successful graph-based representations and algorithms used in language processing and try to explain how and why they work.

Degree:
Note:

Copyright 2008 Association for the Advancement of Artificial Intelligence (AAAI). All rights reserved. http://aaai.org

Note:

Abstract: This article discusses networks and natural language processing. Over the last few years, a number of areas of natural language processing have begun applying graph-based techniques. These include, among others, text summarization, syntactic parsing, word-sense disambiguation, ontology construction, sentiment and subjectivity analysis, and text clustering. In this paper, the authors present some of the most successful graph-based representations and algorithms used in language processing and try to explain how and why they work.

Physical Description:

13 p.

Language(s):
Subject(s):
Keyword(s): natural language processing | graph algorithms
Source: Artificial Intelligence Magazine, 2008. Palo Alto: American Association for Artificial Intelligence, pp. 16-28.
Partner:
UNT College of Engineering
Collection:
UNT Scholarly Works
Identifier:
  • ISSN: 0738-4602
  • ARK: ark:/67531/metadc31008
Resource Type: Article
Format: Text
Rights:
Access: Public
Citation:
Publication Title: Artificial Intelligence Magazine
Volume: 29
Issue: 3
Page Start: 16
Page End: 28
Pages: 13
Peer Reviewed: Yes