code for model described in http://cs.umd.edu/~miyyer/pubs/2015_acl_dan.pdf along with negation dataset (negation_dataset.txt). feel free to email me at miyyer@umd.edu with any comments/problems/questions/suggestions.
- python 2.7.9, numpy 1.9.2 (might work w/ other versions but not tested), nltk
- bash run.sh (downloads word embeddings and dataset, preprocesses PTB trees into DAN format)
- python dan_sentiment.py (can tweak hyperparameters via command-line arguments, currently this runs the fine-grained experiment on only root-level labels and should take a few minutes to finish training)
- each training/test instance must be a tuple with the following format: ([list of word embedding lookup indices associated with text], label)
- if you want to use pretrained word embeddings, you should also pass a pickled matrix using the --We argument, where the matrix is of size d x V (each column stores the embedding for the corresponding word lookup index)
- batch size (the smaller the better, but also slower)
- adagrad initial learning rate (should be decreased as the batch size is decreased)
- word dropout probability (30% is the default but might be too high for some tasks)
- number of epochs (increase when using random initialization)
if you use this code, please cite:
@InProceedings{Iyyer:Manjunatha:Boyd-Graber:III}-2015,
Title = {Deep Unordered Composition Rivals Syntactic Methods for Text Classification},
Booktitle = {Association for Computational Linguistics},
Author = {Mohit Iyyer and Varun Manjunatha and Jordan Boyd-Graber and Hal {Daum\'{e} III}},
Year = {2015},
Location = {Beijing, China}
}