PWCN

PWCN - Proximity-Weighted Convolution Network

Repo for SIGIR 2019 paper titled "Syntax-Aware Aspect-Level Sentiment Classification with Proximity-Weighted Convolution Network"
Chen Zhang, Qiuchi Li 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 and extract glove.840B.300d.txt into glove/.
Train with command, optional arguments could be found in train.py

python train.py --model_name pwcn_dep --dataset laptop

Infer with 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

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

docker run -d --name pwcn auliadil/pwcn
Go to docker image terminal with docker exec -ti pwcn \bin\bash
Run conda activate myenv
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

@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, 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 !

GeneZC / PWCN