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Journal Article

Citation

Roberts K, Harabagiu SM. Biomed. Inform. Insights 2012; 5(Suppl 1): 195-204.

Affiliation

The University of Texas at Dallas, Richardson, TX.

Copyright

(Copyright © 2012, Libertas Academica)

DOI

10.4137/BII.S8958

PMID

22879776

PMCID

PMC3409476

Abstract

In this paper we report on the approaches that we developed for the 2011 i2b2 Shared Task on Sentiment Analysis of Suicide Notes. We have cast the problem of detecting emotions in suicide notes as a supervised multi-label classification problem. Our classifiers use a variety of features based on (a) lexical indicators, (b) topic scores, and (c) similarity measures. Our best submission has a precision of 0.551, a recall of 0.485, and a F-measure of 0.516.


Language: en

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