Fine-grained Sentiment Classification on SST-5 (test)
53.4Accuracy (Fine-grained)CNN-RNF-LSTM
Evaluation results
| Method | Links | ||
|---|---|---|---|
| CNN-RNF-LSTMCategory=CNN variants, Word Vectors=GloVe 300d2018.08 | 53.4 | — | |
| Lei et al., 2017Description=Best published results2018.08 | 53.2 | — | |
| CNN-RNF-GRUCategory=CNN variants, Word Vectors=GloVe 300d2018.08 | 53 | — | |
| NSEword embeddings=300-D Glove 840B2016.07 | 52.8 | — | |
| DMN2016.07 | 52.1 | — | |
| GRU-maxpoolCategory=RNN variants, Word Vectors=GloVe 300d2018.08 | 51.7 | — | |
| LSTM-maxpoolCategory=RNN variants, Word Vectors=GloVe 300d2018.08 | 51.6 | — | |
| CT-LSTM2016.07 | 51 | — | |
| GRUCategory=RNN variants, Word Vectors=GloVe 300d2018.08 | 50.5 | — | |
| LSTMCategory=RNN variants, Word Vectors=GloVe 300d2018.08 | 50.3 | — | |
| DRNN2016.07 | 49.8 | — | |
| Bi-LSTMbidirectional=true2016.07 | 49.1 | — | |
| Paragraph Vector2016.07 | 48.7 | — | |
| CNN-linear-filterCategory=CNN variants, Word Vectors=GloVe 300d2018.08 | 48 | — | |
| CNN-MC2016.07 | 47.4 | — | |
| 2-layer LSTMlayers=22016.07 | 46 | — | |
| RNTN2016.07 | 45.7 | — | |
| Bigram Naïve Bayes2014.05 | — | 0.581 | |
| Matrix Vector RNN2014.05 | — | 0.556 | |
| Naïve Bayes2014.05 | — | 0.59 | |
| Paragraph Vectorcomposition=Concatenation of PV-DBOW and PV-DM2014.05 | — | 0.513 | |
| Recursive Neural Network2014.05 | — | 0.568 | |
| Recursive Neural Tensor Network2014.05 | — | 0.543 | |
| SVMs2014.05 | — | 0.593 | |
| Word Vector Averaging2014.05 | — | 0.673 |