TY  - JOUR
PY  - 2012//
TI  - Topic classification for suicidology
JO  - Journal of computing science and engineering
A1  - Read, Jonathon
A1  - Velldal, Erik
A1  - Øvrelid, Lilja
SP  - 143
EP  - 150
VL  - 6
IS  - 2
N2  - Computational techniques for topic classification can support qualitative research by automatically applying labels in preparation for qualitative analyses. This paper presents an evaluation of supervised learning techniques applied to one such use case, namely, that of labeling emotions, instructions and information in suicide notes. We train a collection of one-versus-all binary support vector machine classifiers, using cost-sensitive learning to deal with class imbalance. The features investigated range from a simple bag-of-words and n-grams over stems, to information drawn from syntactic dependency analysis and WordNet synonym sets. The experimental results are complemented by an analysis of systematic errors in both the output of our system and the gold-standard annotations.  2012. The Korean Institute of Information Scientists and Engineers.<p />
LA  - en
SN  - 1976-4677
UR  - http://dx.doi.org/10.5626/JCSE.2012.6.2.143
ID  - ref1
ER  -