Emotion Cause Identification on Emotion Cause Dataset
76.19PrecisionPAE-DGL
Evaluation results
| Method | Links | |||
|---|---|---|---|---|
| PAE-DGLfeatures=text content, relative position, global label information2019.06 | 76.19 | 69.08 | 72.42 | |
| ConvMS-Memnetarchitecture=convolutional multiple-slot deep memory network2019.06 | 70.76 | 68.38 | 69.55 | |
| RBtype=Rule-based method2019.06 | 67.47 | 42.87 | 52.43 | |
| Multi-Kerneltype=Multi-kernel method2019.06 | 65.88 | 69.27 | 67.52 | |
| CNNarchitecture=Convolutional neural network for sentence classification2019.06 | 62.15 | 59.44 | 60.76 | |
| Memnethops=3, embeddings=pre-trained skip-grams2019.06 | 59.22 | 63.54 | 61.31 | |
| RB+CB+MLtype=Machine learning with rule and common-sense features2019.06 | 59.21 | 53.07 | 55.97 | |
| RB+CBtype=Machine learning with rule and common-sense features2019.06 | 54.35 | 53.07 | 53.7 | |
| Word2vecfeatures=word representations learned by Word2vec2019.06 | 43.01 | 42.33 | 41.36 | |
| SVMfeatures=unigram, bigram, trigram2019.06 | 42 | 43.75 | 42.85 | |
| CBtype=Common-sense based method2019.06 | 26.72 | 71.3 | 38.87 |