PWCN

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Code for SIGIR 2019 paper titled "Syntax-Aware Aspect-Level Sentiment Classification with Proximity-Weighted Convolution Network"

PWCN 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# PWCN

**PWCN** - **P**roximity-**W**eighted **C**onvolution **N**etwork
* Repo for [SIGIR 2019](https://sigir.org/sigir2019/
) paper titled "[Syntax-Aware Aspect-Level Sentiment Classification with Proximity-Weighted Convolution Network](https://arxiv.org/abs/1909.10171)" 
* [Chen Zhang](https://genezc.github.io), [Qiuchi Li](https://qiuchili.github.io) and Dawei Song.

## Requirements

* Python 3.6
* PyTorch 1.0.0
* SpaCy 2.0.18
* numpy 1.15.4

## Usage

* Download pretrained GloVe embeddings with this [link](http://nlp.stanford.edu/data/wordvecs/glove.840B.300d.zip) and extract `glove.840B.300d.txt` into `glove/`.
* Train with command, optional arguments could be found in [train.py](/train.py)
```bash
python train.py --model_name pwcn_dep --dataset laptop
```
* Infer with [infer.py](/infer.py)

## Model

We propose a proximity-weighted convolution network to offer an aspect-specific syntax-aware representation of contexts. In particular, two ways of determining proximity weight are explored, namely position proximity and dependency proximity. The representation is primarily abstracted by a bidirectional LSTM architecture and further enhanced by a proximity-weighted convolution.

An overview of our proposed model is given below

![model](https://raw.githubusercontent.com/GeneZC/PWCN/master/assets/sigir2019pwcn-fig1.png)

## Docker Image

There is a Docker Image created to run this model easily. You can pull the image here

```
docker pull auliadil/pwcn
```

In order to run this docker image, just run 
1. `docker run -d --name pwcn auliadil/pwcn`
2. Go to docker image terminal with `docker exec -ti pwcn \bin\bash`
3. Run `conda activate myenv`
4. Then, you can run the `python train.py --model_name pwcn_dep --dataset laptop` command

## Citation

If you use the code in your paper, please kindly star this repo and cite our paper

```bibtex
@inproceedings{Zhang:2019:SAS:3331184.3331351,
 author = {Zhang, Chen and Li, Qiuchi and Song, Dawei},
 title = {Syntax-Aware Aspect-Level Sentiment Classification with Proximity-Weighted Convolution Network},
 booktitle = {Proceedings of the 42Nd International ACM SIGIR Conference on Research and Development in Information Retrieval},
 series = {SIGIR'19},
 year = {2019},
 isbn = {978-1-4503-6172-9},
 location = {Paris, France},
 pages = {1145--1148},
 numpages = {4},
 url = {http://doi.acm.org/10.1145/3331184.3331351},
 doi = {10.1145/3331184.3331351},
 acmid = {3331351},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {proximity-weighted convolution, sentiment classification, syntax-awareness},
}
```

## Credits

* Code of this repo heavily relies on [ABSA-PyTorch](https://github.com/songyouwei/ABSA-PyTorch), in which I am one of the contributors.
* For any issues or suggestions about this work, don't hesitate to create an issue or directly contact me via [gene_zhangchen@163.com](mailto:gene_zhangchen@163.com) !

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