GWU NLP Lab at SemEval-2019 Task 3 : EmoContext: Effectiveness ofContextual Information in Models for Emotion Detection inSentence-level at Multi-genre Corpus
Shabnam Tafreshi, Mona Diab · 2019
In this paper we present an emotion classifier models that submitted to the SemEval-2019 Task 3 : EmoContext.The task objective is to classify emotion (i.e.happy, sad, angry) in a 3-turn conversational data set.We formulate the task as a classification problem and introduce a Gated Recurrent Neural Network (GRU) model with attention layer, which is bootstrapped with contextual information and trained with a multigenre corpus.We utilize different word embeddings to empirically select the most suited one to represent our features.We train the model with a multigenre emotion corpus to leverage using all available training sets to bootstrap the results.We achieved overall %56.05 f1-score and placed 144.