Codes and Dataset for ACL2017 paper ‘‘An unsupervised neural attention model for aspect extraction’’. (pdf)
THIS FORK: Adoption to TensorFlow backend, Python 3, recent dependency versions
You can find the pre-processed datasets and the pre-trained word embeddings in [Download]. The zip file should be decompressed and put in the main folder.
You can also download the original datasets of Restaurant domain and Beer domain in [Download]. For preprocessing, put the decompressed zip file in the main folder and run
python word2vec.py
python preprocess.py
respectively in code/ . The preprocessed files and trained word embeddings for each domain will be saved in a folder preprocessed_data/.
Under code/ and type the following command for training:
python3 train.py \
--emb ../preprocessed_data/$domain/w2v_embedding \
--domain $domain \
-o output_dir \
where $domain in ['restaurant', 'beer'] is the corresponding domain, --emb is the path to the pre-trained word embeddings, -o is the path of the output directory. You can find more arguments/hyper-parameters defined in train.py with default values used in our experiments.
After training, two output files will be saved in code/output_dir/$domain/: 1) aspect.log contains extracted aspects with top 100 words for each of them. 2) model_param contains the saved model weights
Under code/ and type the following command:
python3 evaluation.py \
--domain $domain \
-o output_dir \
Note that you should keep the values of arguments for evaluation the same as those for training (except --emb, you don't need to specify it), as we need to first rebuild the network architecture and then load the saved model weights.
This will output a file att_weights that contains the attention weights on all test sentences in code/output_dir/$domain.
To assign each test sentence a gold aspect label, you need to first manually map each inferred aspect to a gold aspect label according to its top words, and then uncomment the bottom part in evaluation.py (line 136-144) for evaluaton using F scores.
One example of trained model for the restaurant domain has been put in pre_trained_model/restaurant/, and the corresponding aspect mapping has been provided in evaluation.py (line 136-139). You can uncomment line 28 in evaluation.py and run the above command to evaluate the trained model.
Python 3.6
- keras 2.2
- tensorflow 1.9
- numpy 1.15
- gensim 3.5
Other versions, e.g. slightly older/newer Python 3, may well work, just try it.
You can install all dependencies with the following command:
pip install -r requirements.txt
If you want to pre-process the data yourself, you'll also need NLTK and some of its data, which you can install like this from the Python 3 CLI:
import nltk
nltk.download('stopwords')
nltk.download('wordnet')If you use the code, please cite the following paper:
@InProceedings{he-EtAl:2017:Long2,
author = {He, Ruidan and Lee, Wee Sun and Ng, Hwee Tou and Dahlmeier, Daniel},
title = {An Unsupervised Neural Attention Model for Aspect Extraction},
booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
month = {July},
year = {2017},
address = {Vancouver, Canada},
publisher = {Association for Computational Linguistics}
}