Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Identifying semantic equivalence for multi-document summarisation

Identifying semantic equivalence for multi-document summarisation We describe Semantic Equivalence and Textual Entailment Recognition, and outline a system which uses a number of lexical, syntactic and semantic features to classify pairs of sentences as “semantically equivalent”. We describe an experiment to show how syntactic and semantic features improve the performance of an earlier system, which used only lexical features. We also outline some areas for future work. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Artificial Intelligence Review Springer Journals

Identifying semantic equivalence for multi-document summarisation

Loading next page...
 
/lp/springer-journals/identifying-semantic-equivalence-for-multi-document-summarisation-37zvAvtGYH

References (23)

Publisher
Springer Journals
Copyright
Copyright © 2007 by Springer Science+Business Media B.V.
Subject
Computer Science; Complexity; Computer Science, general ; Artificial Intelligence (incl. Robotics)
ISSN
0269-2821
eISSN
1573-7462
DOI
10.1007/s10462-007-9018-5
Publisher site
See Article on Publisher Site

Abstract

We describe Semantic Equivalence and Textual Entailment Recognition, and outline a system which uses a number of lexical, syntactic and semantic features to classify pairs of sentences as “semantically equivalent”. We describe an experiment to show how syntactic and semantic features improve the performance of an earlier system, which used only lexical features. We also outline some areas for future work.

Journal

Artificial Intelligence ReviewSpringer Journals

Published: Aug 22, 2007

There are no references for this article.