Text Classification

This project implements neural networks for text classification, based on PyTorch (0.4.0).

Data

The original datasets are released by Tang et al. (2015).

Configuration

Word Embedding Layer
- Layer Number: 1
- Dimension: 128
- Pre-Training: Word2Vec / Wiki
Hidden Layer
- Bidirectional (GRNN / LSTM): True
- Convolutional Size (CNN): 5
- Dimension: 128 (64 for each direction for GRNN / LSTM)
Mini-Batch Size: 32
Optimization Algorithm
- Adadelta: lr=1.0, rho=0.9
- SGD: lr=0.001, momentum=0.9
Performance Evaluation
- Leave out the Testing Set
- Five-Fold Cross Validation on the Training Set
  - Four Folds as Training Set
  - One Fold as Validation Set for Early-Stopping
- Predict on the Testing Set
  - Predict with Each of the Five Classifer, and Average the Accuracies
  - Predict with a Classifier Combined by Soft-Voting over the Five Classifiers

Results

Testing Accuracies by Independent Classifers

Model	Pooling	Hierarchical	IMDB(2)	IMDB(10)	Yelp 2013	Yelp 2014
GRNN	Mean	False	0.9056	0.4732	0.6289	0.6318
GRNN	Max	False	0.9067	0.4820	0.6296	0.6337
GRNN	Attention	False	0.9107	0.4872	0.6270	0.6374
LSTM	Mean	False	0.8983	0.4518	0.6156	0.6267
LSTM	Max	False	0.9067	0.4816	0.6273	0.6358
LSTM	Attention	False	0.9073	0.4778	0.6229	0.6362
CNN	Mean	False	0.8974	0.4255	0.6113	0.6156
CNN	Max	False	0.9048	0.4726	0.6187	0.6158
CNN	Attention	False	0.9052	0.4833	0.6197	0.6243
GRNN	Mean	True
GRNN	Max	True
GRNN	Attention	True

Testing Accuracies by Combined Classifer

Model	Pooling	Hierarchical	IMDB(2)	IMDB(10)	Yelp 2013	Yelp 2014
GRNN	Mean	False	0.9174	0.4992	0.6483	0.6448
GRNN	Max	False	0.9212	0.5008	0.6483	0.6495
GRNN	Attention	False	0.9192	0.5038	0.6423	0.6501
LSTM	Mean	False	0.9095	0.4789	0.6321	0.6409
LSTM	Max	False	0.9172	0.5021	0.6483	0.6484
LSTM	Attention	False	0.9152	0.5042	0.6414	0.6483
CNN	Mean	False	0.9046	0.4403	0.6191	0.6216
CNN	Max	False	0.9163	0.4956	0.6333	0.6333
CNN	Attention	False	0.9129	0.4922	0.6303	0.6310
GRNN	Mean	True
GRNN	Max	True
GRNN	Attention	True

References

Tang, D., Qin, B., & Liu, T. (2015). Learning semantic representations of users and products for document level sentiment classification. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (Vol. 1, pp. 1014-1023).

Chen, H., Sun, M., Tu, C., Lin, Y., & Liu, Z. (2016). Neural sentiment classification with user and product attention. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (pp. 1650-1659).

Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., & Hovy, E. (2016). Hierarchical attention networks for document classification. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 1480-1489).

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