Title: Swiss-Chocolate : combining flipout regularization and random forests with artificially built subsystems to boost text-classification for sentiment
Authors : Uzdilli, Fatih
Jaggi, Martin
Egger, Dominic
Julmy, Pascal
Derczynski, Leon
Cieliebak, Mark
Proceedings: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), Denver, Colorado, June 4-5, 2015
Volume(Issue) : 9
Pages : 608
Pages to: 612
Conference details: International Workshop on Semantic Evaluation
Publisher / Ed. Institution : Association for Computational Linguistics
Issue Date: 2015
License (according to publishing contract) : Licence according to publishing contract
Type of review: Peer review (Publication)
Language : English
Subject (DDC) : 004: Computer science
005: Computer programming, programs and data
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: Institute of Applied Information Technology (InIT)
Publication type: Conference Paper
DOI : 10.18653/v1/S15-2101
URI: https://digitalcollection.zhaw.ch/handle/11475/1881
Appears in Collections:Publikationen School of Engineering

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