Learning Document Embeddings by Predicting N-grams for Sentiment Classification of Long Movie Reviews
Table 4: Comparison of DV-ngram with other models. 555Result of DCNN is reported by Iyyer et al. (2015). Results of RNN-LM and PV are reported by Mesnil et al. (2014)
['[BOLD] Bag-of-ngram based models', '[BOLD] Accuracy']
Especially, DV-tri outperforms PV 3.41 percent in terms of accuracy. This result shows that the prediction of word sequences is important for document embeddings. Note that even the simplest DV-uni (use words alone with no n-gram feature) outperforms PV 0.87 percent in terms of accuracy.