Labeling Emotions in Suicide Notes: Cost-Sensitive Learning with Heterogeneous Features

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 explor...

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Detalles Bibliográficos
Autores principales: Read, Jonathon, Velldal, Erik, Øvrelid, Lilja
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Libertas Academica 2012
Materias:
Original Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3409483/
https://www.ncbi.nlm.nih.gov/pubmed/22879765
http://dx.doi.org/10.4137/BII.S8930
Descripción
Sumario: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.

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