@article{ref1,
title="Labeling emotions in suicide notes: cost-sensitive learning with heterogeneous features",
journal="Biomedical informatics insights",
year="2012",
author="Read, Jonathon and Velldal, Erik and Ovrelid, Lilja",
volume="5",
number="Suppl 1",
pages="99-103",
abstract="This paper describes a system developed for Track 2 of the 2011 Medical NLP Challenge on identifying emotions in suicide notes. Our approach involves learning a collection of one-versus-all classifiers, each deciding whether or not a particular label should be assigned to a given sentence. We explore a variety of features types-syntactic, semantic and surface-oriented. Cost-sensitive learning is used for dealing with the issue of class imbalance in the data.<p /> <p>Language: en</p>",
language="en",
issn="1178-2226",
doi="10.4137/BII.S8930",
url="http://dx.doi.org/10.4137/BII.S8930"
}