Abstract
The need for non-standard text categorisation, i.e. based on some subtle criterion other than topics, may arise in various circumstances. In this study, we consider written responses to a standardised psychometric test for determining the personality trait of human subjects. A number of state-of-the-art text classifiers that have been very successful in standard topic-based classification problems turn out to perform poorly in this task. Here we propose a very simple probabilistic approach, which is able to achieve accurate predictions, and demonstrates this peculiar problem is still solvable by simple statistical text representation means. We then extend this approach to include a latent variable, in order to obtain additional explanatory information beyond a black-box prediction.
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Kabán, A. (2008). A Probabilistic Neighbourhood Translation Approach for Non-standard Text Categorisation. In: Jean-Fran, JF., Berthold, M.R., Horváth, T. (eds) Discovery Science. DS 2008. Lecture Notes in Computer Science(), vol 5255. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88411-8_31
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DOI: https://doi.org/10.1007/978-3-540-88411-8_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88410-1
Online ISBN: 978-3-540-88411-8
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