Abstract
This paper describes the entry NUIG in the WASSA 2017 shared task on emotion 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 features, 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.
Cite
CITATION STYLE
Andryushechkin, V., Wood, I. D., & O’Neill, J. (2017). NUIG at EmoInt-2017: BiLSTM and SVR ensemble to detect emotion intensity. In EMNLP 2017 - 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, WASSA 2017 - Proceedings of the Workshop (pp. 175–179). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/w17-5223
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