|Title:||Swiss-Chocolate : combining flipout regularization and random forests with artificially built subsystems to boost text-classification for sentiment|
|Authors :||Uzdilli, Fatih|
|Proceedings:||Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), Denver, Colorado, June 4-5, 2015|
|Conference details:||International Workshop on Semantic Evaluation|
|Publisher / Ed. Institution :||Association for Computational Linguistics|
|Language :||Englisch / English|
|Subject (DDC) :||004: Informatik|
005: Computerprogramme, Datenverarbeitung
|Abstract:||We describe a classifier for predicting message-level sentiment of English microblog messages from Twitter. This paper describes our submission to the SemEval-2015 competition (Task 10). Our approach is to combine several variants of our previous year’s SVM system into one meta-classifier, which was then trained using a random forest. The main idea is that the meta-classifier allows the combination of the strengths and overcome some of the weaknesses of the artificially-built individual classifiers, and adds additional non-linearity. We were also able to improve the linear classifiers by using a new regularization technique we call flipout.|
|Departement:||School of Engineering|
|Organisational Unit:||Institut für Angewandte Informationstechnologie (InIT)|
|Publication type:||Konferenz: Paper / Conference Paper|
|Appears in Collections:||Publikationen School of Engineering|
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