By Topic

Comparing measures of 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
$31 $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

4 Author(s)
Ljubesic, N. ; Fac. of Humanities & Social Sci., Dept. of Inf. Sci., Univ. of Zagreb, Zagreb ; Boras, D. ; Bakaric, N. ; Njavro, J.

The aim of this paper is to compare different methods for automatic extraction of semantic similarity measures from corpora. The semantic similarity measure is proven to be very useful for many tasks in natural language processing like information retrieval, information extraction, machine translation etc. Additionally, one of the main problems in natural language processing is data sparseness since no language sample is large enough to seize all possible language combinations. In our research we experiment with four different measures of association with context and eight different measures of vector similarity. The results show that the Jensen-Shannon divergence and L1 and L2 norm outperform other measures of vector similarity regardless of the measure of association with context used. Maximum likelihood estimate and t-test show better results than other measures of association with context.

Published in:

Information Technology Interfaces, 2008. ITI 2008. 30th International Conference on

Date of Conference:

23-26 June 2008