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Categorizing children: automated text classification of CHILDES files

Opsomer, Rob; Knoth, Petr; van Polen, Freek; Trapman, Jantine and Wiering, Marco (2008). Categorizing children: automated text classification of CHILDES files. In: The 20th Belgian-Netherlands Conference on Artificial Intelligence (BNAIC 2008), 30-31 Oct 2008, Enchede, The Netherlands.

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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 or Workshop Item
Copyright Holders: 2008 Universiteit Twente, Enschede
ISSN: 1568-7805
Extra Information: Proceedings of the twentieth Belgian-Dutch Conference on
Artificial Intelligence.
Enschede, October 30-31, 2008.
Anton Nijholt, Maja Pantic, Mannes Poel and Hendri Hondorp (eds.)
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Research Group: Centre for Research in Computing (CRC)
Big Scientific Data and Text Analytics Group (BSDTAG)
Related URLs:
Item ID: 24749
Depositing User: Kay Dave
Date Deposited: 19 Nov 2010 09:24
Last Modified: 03 Apr 2019 21:36
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