Resumen de NUIG at EmoInt-2017: BiLSTM and SVR Ensemble to Detect Emotion Intensity
Vladimir Andryushechkin, Ian David Wood, James O’Neill
This paper describes the entry NUIG in the WASSA 20171 shared task on emo- tion recognition. The NUIG system used an SVR (SVM regression) and BiLSTM ensemble, utilizing primarily n-grams (for SVR features) and tweet word embeddings (for BiLSTM features). Experiments were carried out on several other candidate fea- tures, some of which were added to the SVR model. Parameter selection for the SVR model was run as a grid search whilst parameters for the BiLSTM model were selected through a non-exhaustive ad-hoc search.
