Opsomer, Rob; Knoth, Petr; van Polen, Freek; Trapman, Jantine and Wiering, Marco
Due to copyright restrictions, this file is not available for public download
|Google Scholar:||Look up in Google Scholar|
In this paper we present the application of machine learning text classification methods to two tasks: categorization of children's speech in the CHILDES Database according to gender and age. Both tasks are binary. For age, we distinguish two age groups between the age of 1.9 and 3.0 years old. The boundary between the groups lies at the age of 2.4 which is both the mean and the median of the age in our data set. We show that the machine learning approach, based on a bag of words, can achieve much better results than features such as average utterance length or Type-Token Ratio, which are methods traditionally used by linguists. We have achieved 80.5% and 70.5% classification accuracy for the age and gender task respectively.
|Item Type:||Conference Item|
|Copyright Holders:||2008 Universiteit Twente, Enschede|
|Extra Information:||Proceedings of the twentieth Belgian-Dutch Conference on
Enschede, October 30-31, 2008.
Anton Nijholt, Maja Pantic, Mannes Poel and Hendri Hondorp (eds.)
|Academic Unit/Department:||Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
|Interdisciplinary Research Centre:||Centre for Research in Computing (CRC)|
|Depositing User:||Kay Dave|
|Date Deposited:||19 Nov 2010 09:24|
|Last Modified:||06 Aug 2016 20:25|
|Share this page:|