A Self-Supervised Contrastive Learning Framework for Aspect Detection

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Deep Learning AspDecSSCL
Overview

AspDecSSCL

A Self-Supervised Contrastive Learning Framework for Aspect Detection

image image image image image image

This repository is a pytorch implementation for the following AAAI'21 paper:

A Simple and Effective Self-Supervised Contrastive Learning Framework for Aspect Detection

Tian Shi, Liuqing Li, Ping Wang, Chandan K. Reddy

Video Presentation

Requirements

  • Python 3.6.9
  • argparse=1.1
  • torch=1.4.0
  • sklearn=0.22.2.post1
  • numpy=1.18.2
  • gensim=3.8.3

Dataset

You can download processed dataset from here. Place them along side with AapDecSSCL.

|--- AspDecSSCL
|--- Data
|    |--- bags_and_cases
|    |--- restaurant
|    |    |--- dev.txt
|    |    |--- test.txt
|    |    |--- train.txt
|    |    |--- train_w2v.txt
|--- cluster_results (results, automatically build)
|--- nats_results (results, automatically build)
|

Train your model from scratch

Prepare word and aspect embeddings.

Train word2vec: python3 run.py --task word2vec

Run Kmeans: python3 run.py --task kmeans

Check Kmeans Keywords python3 run.py --task kmeans-keywords

Self-supervised Learning (Teacher Model)

SSCL Training python3 run.py --task sscl-train

Before validation, you need to perform aspect mapping. There is a file aspect_mapping.txt in nats_results. For general, please change nomap to none. Other aspects should use their names. Please check test.txt to validate the names.

SSCL validation python3 run.py --task sscl-validate

SSCL testing python3 run.py --task sscl-test

SSCL evaluate python3 run.py --task sscl-evaluate

SSCL teacher python3 run.py --task sscl-teacher

SSCL clean results python3 run.py --task sscl-clean

Student Model

SSCLS training python3 run.py --task student-train

SSCLS validation python3 run.py --task student-validate

SSCLS testing python3 run.py --task student-test

SSCLS testing python3 run.py --task student-evaluate

SSCLS clean python3 run.py --task student-clean

Citation

@article{shi2020simple,
  title={A Simple and Effective Self-Supervised Contrastive Learning Framework for Aspect Detection},
  author={Shi, Tian and Li, Liuqing and Wang, Ping and Reddy, Chandan K},
  journal={arXiv preprint arXiv:2009.09107},
  year={2020}
}
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Comments
  • Custom Dataset Preparation

    Custom Dataset Preparation

    Dear authors: Thank you for providing your full code. I would like to know if I want to use sscl on my own custom, unlabeled dataset to get inferred aspects, how can I prepare my data to fit the format of your processed dataset? The train, test files include entries like this: {"id": "B00011FIBO-0001-0-1", "text_reg": "because of the comments on actiontec website .", "text_bert": "because of the comments on action ##tec website .", "text_uae": "comment actiontec website"} I wonder what text_reg, text_bert, text_uae are?
    Thank you for your time!

    opened by Nana2929 1
Owner
Tian Shi
NLP and machine learning for news and online reviews.
Tian Shi
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