BrainEE at SemEval-2019 Task 3: Ensembling Linear Classifiers for Emotion Prediction
Vachagan Gratian · 2019
We present a homogeneous ensemble of linear perceptrons trained for emotion classification as part of the SemEval-2019 sharedtask 3. The model uses a matrix of probabilities to weight the activations of the baseclassifiers and makes a final prediction using the sum rule.The base-classifiers are multi-class perceptrons utilizing character and word n-grams, part-of-speech tags and sentiment polarity scores.The results of our experiments indicate that the ensemble outperforms the base-classifiers, but only marginally.In the best scenario our model attains an F-Micro score 1 of 0.672, whereas the base-classifiers attained scores ranging from 0.636 to 0.666.