By Topic

Dirichlet Process Mixture Models for lexical category acquisition

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

3 Author(s)
Bichuan Zhang ; Center for Intell. Sci. & Technol., Beijing Univ. of Posts & Telecommun., Beijing, China ; Xiaojie Wang ; Guannan Fang

In this work, we apply Dirichlet Process Mixture Models (DPMMs) to a cognitive computational task in natural language processing (NLP): lexical category acquisition. The model takes a corpus of child-directed speech from CHILDES as input. We assess the performance using a new measure we proposed that meets three criteria: informativeness, diversity and purity. The quantitative and qualitative evaluation performed highlights the choice of the feature dimension and inherent parameters can influence the performance of DPMMs towards lexical category solutions.

Published in:

Cloud Computing and Intelligence Systems (CCIS), 2011 IEEE International Conference on

Date of Conference:

15-17 Sept. 2011