# abae-pytorch

**Repository Path**: li_zhong_yun/abae-pytorch

## Basic Information

- **Project Name**: abae-pytorch
- **Description**: PyTorch implementation of 'An Unsupervised Neural Attention Model for Aspect Extraction' by He et al. ACL2017'
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-09-14
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# ABAE-PyTorch

Yet another PyTorch implementation of the model described in the paper [**An Unsupervised Neural Attention Model for Aspect Extraction**](https://aclweb.org/anthology/papers/P/P17/P17-1036/) by He, Ruidan and  Lee, Wee Sun  and  Ng, Hwee Tou  and  Dahlmeier, Daniel, **ACL2017**.

## Example

**For a working example of a whole pipeline please refer to `example_run.sh`**

Let's get some data:

```
wget http://snap.stanford.edu/data/amazon/productGraph/categoryFiles/reviews_Electronics_5.json.gz
gunzip reviews_Electronics_5.json.gz    
python3 custom_format_converter.py reviews_Electronics_5.json
```

Then we need to train the word vectors:
    
```
python3 word2vec.py reviews_Electronics_5.json.txt
```
And run 

```
usage: main.py [-h] [--word-vectors-path <str>] [--batch-size BATCH_SIZE]
               [--aspects-number ASPECTS_NUMBER] [--ortho-reg ORTHO_REG]
               [--epochs EPOCHS] [--optimizer {adam,adagrad,sgd}]
               [--negative-samples NEG_SAMPLES] [--dataset-path DATASET_PATH]
               [--maxlen MAXLEN]

optional arguments:
  -h, --help            show this help message and exit
  --word-vectors-path <str>, -wv <str>
                        path to word vectors file
  --batch-size BATCH_SIZE, -b BATCH_SIZE
                        Batch size for training
  --aspects-number ASPECTS_NUMBER, -as ASPECTS_NUMBER
                        A total number of aspects
  --ortho-reg ORTHO_REG, -orth ORTHO_REG
                        Ortho-regularization impact coefficient
  --epochs EPOCHS, -e EPOCHS
                        Epochs count
  --optimizer {adam,adagrad,sgd}, -opt {adam,adagrad,sgd}
                        Optimizer
  --negative-samples NEG_SAMPLES, -ns NEG_SAMPLES
                        Negative samples per positive one
  --dataset-path DATASET_PATH, -d DATASET_PATH
                        Path to a training texts file. One sentence per line,
                        tokens separated wiht spaces.
  --maxlen MAXLEN, -l MAXLEN
                        Max length of the considered sentence; the rest is
                        clipped if longer

```

For a working example of a whole pipeline please refer to `example_run.sh` 

I acknowledge the implementation is raw, code modification requests and issues are welcome.

## Thanks for contributions
[@alexdevmotion](https://github.com/alexdevmotion)

## TODOs

* Evaluation: PMI, NPMI, LCP, L1/L2/coord/cosine (Nikolenko SIGIR'16), ...
* Aspects prediction on text + visualization
* Saving the model, aspects, etc.

## How to cite

Not neccessary, but greatly appreciated, if you use this work.

```latex
@misc{abaepytorch2019alekseev,
  title     = {{alexeyev/abae-pytorch: ABAE, PyTorch implementation}},
  year      = {2019},
  url       = {https://github.com/alexeyev/abae-pytorch},
  language  = {english}
}
```

## Please also see

* Reference [Keras implementation](https://github.com/ruidan/Unsupervised-Aspect-Extraction)
* Updated [Keras version](https://github.com/madrugado/Attention-Based-Aspect-Extraction), compliant with Keras 2.x and Python 3.x.