https://github.com/cm-bf/gcae

A refresh version of GCAE for pytorch>=1.0 with a better README.
https://github.com/cm-bf/gcae

Last synced: 23 days ago
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A refresh version of GCAE for pytorch>=1.0 with a better README.

• Host: GitHub
• URL: https://github.com/cm-bf/gcae
• Owner: CM-BF
• Created: 2020-02-12T08:15:48.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2020-02-12T08:33:54.000Z (almost 5 years ago)
• Last Synced: 2024-10-15T19:07:46.185Z (2 months ago)
• Language: Python
• Size: 425 KB
• Stars: 3
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# This is a refresh version of GCAE

## requirements

* CUDA 10.0

* pytorch >= 1.0

* other packages: torchtext, nltk==3.2.5, sacremoses, simplejson

## Resources

GloVe: glove.840B.300d.txt (Please change the 2 paths in w2y.py)

Following the notice to download the nltk resources when you run the code.

Datasets:

* SemEval 2014 Task4

* SemEval 2015 Task12

* SemEval 2016 Task5

Please find them on their official website, and they will finally lead you to Metashare to download (you need a free account). To verify whether you have downloaded the right .xml file, please check 13-25 lines in getsemeval.py. You may have to change the name of 2015's .xml files.

## Run ACSA for test

```

python -m run -lr 1e-2 -batch-size 32 -verbose 1 -model CNN_Gate_Aspect -embed_file glove -r_l r -epochs 13

```

The following message is the original README file. (from https://github.com/wxue004cs/GCAE)

# Code and data for Aspect Based Sentiment Analysis with Gated Convolutional Networks

```

@inproceedings{DBLP:conf/acl/LiX18,

  author    = {Wei Xue and Tao Li},

  title     = {Aspect Based Sentiment Analysis with Gated Convolutional Networks},

  booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational

               Linguistics, {ACL} 2018, Melbourne, Australia, July 15-20, 2018, Volume

               1: Long Papers},

  pages     = {2514--2523},

  year      = {2018},

  crossref  = {DBLP:conf/acl/2018-1},

  url       = {https://aclanthology.info/papers/P18-1234/p18-1234},

  timestamp = {Thu, 12 Jul 2018 14:15:56 +0200},

  biburl    = {https://dblp.org/rec/bib/conf/acl/LiX18},

  bibsource = {dblp computer science bibliography, https://dblp.org}

}

```

# Instructions:

Download glove or word2vec file and change the path in w2v.py correspondingly.

## ACSA

python -m run -lr 1e-2 -batch-size 32  -verbose 1  -model CNN_Gate_Aspect    -embed_file glove  -r_l r  -epochs 13

python -m run -lr 1e-2 -batch-size 32  -verbose 1  -model CNN_Gate_Aspect    -embed_file glove  -r_l r  -year 14 -epochs 5

## ATSA

python -m run -lr 5e-3 -batch-size 32  -verbose 1  -model CNN_Gate_Aspect  -embed_file glove  -r_l r -year 14 -epochs 6 -atsa

python -m run -lr 5e-3 -batch-size 32  -verbose 1  -model CNN_Gate_Aspect  -embed_file glove  -r_l l -year 14 -epochs 5 -atsa