NUIG at EmoInt-2017: BiLSTM and SVR Ensemble to Detect Emotion Intensity
暂无分享,去创建一个
[1] Erik Cambria,et al. A review of affective computing: From unimodal analysis to multimodal fusion , 2017, Inf. Fusion.
[2] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[3] Saif Mohammad,et al. WASSA-2017 Shared Task on Emotion Intensity , 2017, WASSA@EMNLP.
[4] Björn W. Schuller,et al. Combining speech recognition and acoustic word emotion models for robust text-independent emotion recognition , 2008, 2008 IEEE International Conference on Multimedia and Expo.
[5] Jeffrey Pennington,et al. GloVe: Global Vectors for Word Representation , 2014, EMNLP.
[6] Björn W. Schuller,et al. Abandoning emotion classes - towards continuous emotion recognition with modelling of long-range dependencies , 2008, INTERSPEECH.
[7] Saif Mohammad,et al. #Emotional Tweets , 2012, *SEMEVAL.
[8] Chung-Hsien Wu,et al. Emotion recognition from text using semantic labels and separable mixture models , 2006, TALIP.
[9] P. Ekman. An argument for basic emotions , 1992 .