Binary classifiers and latent sequence models for emotion detection in suicide notes

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DOIResolve DOI: http://doi.org/10.4137/BII.S8933
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TypeArticle
Journal titleBiomedical Informatics Insights
Volume5
IssueSupplement 1
Pages147154; # of pages: 8
AbstractThis paper describes the National Research Council of Canada’s submission to the 2011 i2b2 NLP challenge on the detection of emotions in suicide notes. In this task, each sentence of a suicide note is annotated with zero or more emotions, making it a multi-label sentence classification task. We employ two distinct large-margin models capable of handling multiple labels. The first uses one classifier per emotion, and is built to simplify label balance issues and to allow extremely fast development. This approach is very effective, scoring an F-measure of 55.22 and placing fourth in the competition, making it the best system that does not use web-derived statistics or re-annotated training data. Second, we present a latent sequence model, which learns to segment the sentence into a number of emotion regions. This model is intended to gracefully handle sentences that convey multiple thoughts and emotions. Preliminary work with the latent sequence model shows promise, resulting in comparable performance using fewer features.
Publication date
LanguageEnglish
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedYes
NPARC number20262889
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Record identifier9d8c88cd-dd46-4c75-96fc-63bdb095b3c4
Record created2012-07-10
Record modified2016-05-09
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