Semantic Document Engineering with WordNet and PageRank

Description:

This article discusses semantic document engineering with WordNet and PageRank.

Creator(s):
Creation Date: March 2005
Partner(s):
UNT College of Engineering
Collection(s):
UNT Scholarly Works
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Total Uses: 160
Past 30 days: 3
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Creator (Author):
Tarau, Paul

University of North Texas

Creator (Author):
Mihalcea, Rada, 1974-

University of North Texas

Creator (Author):
Figa, Elizabeth

University of North Texas

Date(s):
  • Creation: March 2005
Description:

This article discusses semantic document engineering with WordNet and PageRank.

Degree:
Note:

Abstract: This paper describes natural language processing techniques for document engineering in combination with graph algorithms and statistical methods. Google's PageRank and similar fast-converging recursive graph algorithms have provided practical means to statistically rank vertices of large graphs like the World Wide Web. By combining a fast Java-based PageRank implementation with a Prolog base inferential layer, running on top of an optimized WordNet graph, the authors describe applications to word sense disambiguation and evaluate their accuracy on standard benchmarks.

Physical Description:

5 p.

Language(s):
Subject(s):
Keyword(s): word sense disambiguations | PageRank-style graph algorithms | WordNet | semantics-based document processing | logic programming | natural language processing
Source: Association for Computing Machinery (ACM) Symposium on Applied Computing (SAC), 2005, Santa Fe, New Mexico, United States
Contributor(s):
Partner:
UNT College of Engineering
Collection:
UNT Scholarly Works
Identifier:
  • ARK: ark:/67531/metadc30974
Resource Type: Paper
Format: Text
Rights:
Access: Public