By Topic

Multi-word complex concept retrieval via lexical semantic similarity

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)

This paper first presents a simple computational means of measuring universal object similarity that is based on classical feature-based similarity models. This computational model is implemented with the help of semantic network representations (e.g. WordNet taxonomy) and corpus statistics. It is then extended and applied to a higher level and practical information retrieval task-retrieving multi-word complex concepts. The extension is performed by pair-wise comparison of all decomposed sub-concepts or terms in a query and the texts, trying different schemes for combining averaging and maximization of the pair-wise similarities. Series of experiments are conducted to compare it with classic statistical methods and the results are supportive of our work

Published in:

Information Intelligence and Systems, 1999. Proceedings. 1999 International Conference on

Date of Conference: