Duluth UROP at SemEval-2018 Task 2: Multilingual Emoji Prediction with Ensemble Learning and Oversampling

Shuning Jin, Ted Pedersen


Abstract
This paper describes the Duluth UROP systems that participated in SemEval–2018 Task 2, Multilingual Emoji Prediction. We relied on a variety of ensembles made up of classifiers using Naive Bayes, Logistic Regression, and Random Forests. We used unigram and bigram features and tried to offset the skewness of the data through the use of oversampling. Our task evaluation results place us 19th of 48 systems in the English evaluation, and 5th of 21 in the Spanish. After the evaluation we realized that some simple changes to our pre-processing could significantly improve our results. After making these changes we attained results that would have placed us sixth in the English evaluation, and second in the Spanish.
Anthology ID:
S18-1077
Volume:
Proceedings of The 12th International Workshop on Semantic Evaluation
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
482–485
Language:
URL:
https://www.aclweb.org/anthology/S18-1077
DOI:
10.18653/v1/S18-1077
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S18-1077.pdf