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- W2955049062 abstract "In this paper we present our submission for SemEval-2019 Task 3: EmoContext. The task consisted of classifying a textual dialogue into one of four emotion classes: happy, sad, angry or others. Our approach tried to improve on multiple aspects, preprocessing with an emphasis on spell-checking and ensembling with four different models: Bi-directional contextual LSTM (BC-LSTM), categorical Bi-LSTM (CAT-LSTM), binary convolutional Bi-LSTM (BIN-LSTM) and Gated Recurrent Unit (GRU). On the leader-board, we submitted two systems that obtained a micro F1 score (F1μ) of 0.711 and 0.712. After the competition, we merged our two systems with ensembling, which achieved a F1μ of 0.7324 on the test dataset." @default.
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- W2955049062 date "2019-01-01" @default.
- W2955049062 modified "2023-09-28" @default.
- W2955049062 title "SCIA at SemEval-2019 Task 3: Sentiment Analysis in Textual Conversations Using Deep Learning" @default.
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- W2955049062 doi "https://doi.org/10.18653/v1/s19-2051" @default.
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