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- W2806626040 abstract "We present our methods and results for affect analysis in Twitter developed as a part of SemEval-2018 Task 1, where the sub-tasks involve predicting the intensity of emotion, the intensity of sentiment, and valence for tweets. For modeling, though we use a traditional LSTM network, we combine our model with several state-of-the-art techniques to improve its performance in a low-resource setting. For example, we use an encoder-decoder network to initialize the LSTM weights. Without any task specific optimization we achieve competitive results (macro-average Pearson correlation coefficient 0.696) in the El-reg task. In this paper, we describe our development strategy in detail along with an exposition of our results." @default.
- W2806626040 created "2018-06-13" @default.
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- W2806626040 date "2018-01-01" @default.
- W2806626040 modified "2023-09-23" @default.
- W2806626040 title "RIDDL at SemEval-2018 Task 1: Rage Intensity Detection with Deep Learning" @default.
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- W2806626040 doi "https://doi.org/10.18653/v1/s18-1054" @default.
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